diff --git a/README.md b/README.md index 512be46e1..46a587aa4 100644 --- a/README.md +++ b/README.md @@ -32,10 +32,11 @@ Welcome to the official repository for the Microsoft AI Agentic Workshop! This r ## Key Features - **[Microsoft Agent Framework](https://github.com/microsoft/agent-framework) Integration** - Single-agent, multi-agent Magentic orchestration, and handoff-based domain routing with MCP tools. [Pattern guide →](agentic_ai/agents/agent_framework/README.md) -- **[Workflow Orchestration](agentic_ai/workflow/)** - Pregel-style execution, checkpointing, human-in-the-loop patterns, and real-time observability. [Fraud Detection Demo →](agentic_ai/workflow/fraud_detection/) -- **Advanced UI Options** - React frontend with streaming visualization or Streamlit for quick prototyping +- **[Workflow Orchestration](agentic_ai/workflow/)** - Hybrid Workflow + Durable Task architecture with fan-out/fan-in topology, human-in-the-loop, and real-time observability. [Fraud Detection Demo →](agentic_ai/workflow/fraud_detection_durable/) +- **[Observability with Application Insights](agentic_ai/observability/)** - Full tracing of agent executions, tool calls, and LLM invocations with pre-built Grafana dashboards. [Setup Guide →](agentic_ai/observability/README.md) +- **Advanced UI Options** - React frontend with interactive workflow visualization and step-by-step tool call details - **[MCP Server Integration](mcp/)** - Model Context Protocol for enhanced agent tool capabilities with advanced features: authentication, RBAC, and APIM integration -- **[Emerging Agentic Scenarios](agentic_ai/scenarios/)** - Long-running workflows, progress updates, and durable agent patterns +- **[Agent Evaluations](agentic_ai/evaluations/)** - Evaluate agent performance with custom metrics and test datasets - **Agent State & History Persistence** - In-memory or CosmosDB backend for conversation history and agent state - **[Enterprise-Ready Reference Architecture](infra/README.md)** - Production-grade deployment with VNet integration, private endpoints, managed identity, Terraform/Bicep IaC, and GitHub Actions CI/CD @@ -46,7 +47,7 @@ Welcome to the official repository for the Microsoft AI Agentic Workshop! This r 1. Review the [Setup Instructions](./SETUP.md) for environment prerequisites and step-by-step installation. 2. Explore the [Business Scenario and Agent Design](./SCENARIO.md) to understand the workshop challenge. 3. Check out the **[Agent Framework Implementation Patterns](agentic_ai/agents/agent_framework/README.md)** to choose the right multi-agent approach (single-agent, Magentic orchestration, or handoff pattern). -4. Try the **[Fraud Detection Workflow Demo](agentic_ai/workflow/fraud_detection/)** to see enterprise orchestration patterns in action. +4. Try the **[Durable Fraud Detection Workflow](agentic_ai/workflow/fraud_detection_durable/)** to see hybrid Workflow + Durable Task orchestration with human-in-the-loop. 5. Dive into [System Architecture](./ARCHITECTURE.md) before building and customizing your agent solutions. 6. Utilize the [Support Guide](./SUPPORT.md) for troubleshooting and assistance. diff --git a/agentic_ai/applications/.env.sample b/agentic_ai/applications/.env.sample index 6608c22cc..a014a428f 100644 --- a/agentic_ai/applications/.env.sample +++ b/agentic_ai/applications/.env.sample @@ -98,3 +98,20 @@ MAGENTIC_MAX_ROUNDS=10 # 0 = no context transfer (domain isolation) # N = transfer last N turns (1 turn = user message + assistant response) HANDOFF_CONTEXT_TRANSFER_TURNS=-1 + +############################################ +# Application Insights Observability # +############################################ +# Connection string from Azure Portal > Application Insights > Overview > Connection String +# APPLICATIONINSIGHTS_CONNECTION_STRING="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/;LiveEndpoint=https://xxx.livediagnostics.monitor.azure.com/" + +# Enable sensitive data in traces (prompts, responses) - DEV ONLY! +# ENABLE_SENSITIVE_DATA=true + +# Service name for telemetry (appears in Application Insights and Grafana) +# OTEL_SERVICE_NAME="contoso-agent" + +# Grafana Dashboards (after setting up Azure Managed Grafana): +# - Agent Overview: https://aka.ms/amg/dash/af-agent +# - Workflow Overview: https://aka.ms/amg/dash/af-workflow + diff --git a/agentic_ai/applications/backend.py b/agentic_ai/applications/backend.py index 85cddef08..45083b94d 100644 --- a/agentic_ai/applications/backend.py +++ b/agentic_ai/applications/backend.py @@ -15,6 +15,9 @@ from pathlib import Path from typing import Dict, List, Any, Optional, Set, DefaultDict from collections import defaultdict + +# Add parent directory to path for observability module +sys.path.insert(0, str(Path(__file__).parent.parent)) import httpx import jwt @@ -29,10 +32,29 @@ from dotenv import load_dotenv # ------------------------------------------------------------------ -# Environment +# Environment (load first so observability can read connection string) # ------------------------------------------------------------------ load_dotenv() # read .env if present +# ------------------------------------------------------------------ +# Observability (must be before any agent imports) +# ------------------------------------------------------------------ +from observability import setup_observability + +# Initialize Application Insights tracing if configured +# All agents (single, reflection, handoff, etc.) are automatically traced +_observability_enabled = setup_observability( + service_name="contoso-agent-backend", + enable_live_metrics=True, + enable_sensitive_data=os.getenv("ENABLE_SENSITIVE_DATA", "false").lower() in ("1", "true", "yes"), +) +if _observability_enabled: + logging.getLogger(__name__).info("✅ Application Insights observability enabled") + +# ------------------------------------------------------------------ +# Auth Configuration +# ------------------------------------------------------------------ + # Feature flag: disable auth for local dev / demos DISABLE_AUTH = os.getenv("DISABLE_AUTH", "false").lower() in ("1", "true", "yes") diff --git a/agentic_ai/applications/pyproject.toml b/agentic_ai/applications/pyproject.toml index c8f6dfa01..eacd63989 100644 --- a/agentic_ai/applications/pyproject.toml +++ b/agentic_ai/applications/pyproject.toml @@ -9,6 +9,7 @@ dependencies = [ "azure-ai-evaluation>=1.14.0", "azure-ai-projects>=2.0.0b2", "azure-cosmos==4.9.0", + "azure-monitor-opentelemetry>=1.6.0", "fastapi==0.115.12", "flasgger==0.9.7.1", "flask==3.0.3", diff --git a/agentic_ai/applications/uv.lock b/agentic_ai/applications/uv.lock index 022275724..47af74d72 100644 --- a/agentic_ai/applications/uv.lock +++ b/agentic_ai/applications/uv.lock @@ -499,6 +499,7 @@ dependencies = [ { name = "azure-ai-evaluation" }, { name = "azure-ai-projects" }, { name = "azure-cosmos" }, + { name = "azure-monitor-opentelemetry" }, { name = "fastapi" }, { name = "flasgger" }, { name = "flask" }, @@ -529,6 +530,7 @@ 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help="Backend URL to send requests to") parser.add_argument("--remote", action="store_true", help="Run evaluation in Azure AI Foundry portal only (skip local)") parser.add_argument("--local", action="store_true", help="Run local evaluation only (default if neither specified)") parser.add_argument("--limit", type=int, default=0, help="Limit number of test cases to run (0 = all)") diff --git a/agentic_ai/observability/README.md b/agentic_ai/observability/README.md new file mode 100644 index 000000000..fb030f79f --- /dev/null +++ b/agentic_ai/observability/README.md @@ -0,0 +1,198 @@ +# Agent Observability with Application Insights + +This module provides full observability for Agent Framework applications using Azure Application Insights and Grafana dashboards. + +## Features + +- **Traces**: Full span hierarchy for agent executions, tool calls, and LLM invocations +- **Logs**: Structured logging with trace context correlation +- **Metrics**: Token usage, latency, and custom metrics +- **Grafana Dashboards**: Pre-built dashboards for agent and workflow visualization + +## Quick Start + +### 1. Get your Application Insights Connection String + +From Azure Portal: +1. Navigate to your Application Insights resource +2. Go to **Overview** → **Connection String** +3. Copy the full connection string + +### 2. Configure Environment + +Add to your `.env` file (in `agentic_ai/applications/`): + +```bash +APPLICATIONINSIGHTS_CONNECTION_STRING="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/;LiveEndpoint=https://xxx.livediagnostics.monitor.azure.com/" + +# Optional: Enable sensitive data (prompts/responses) - DEV ONLY! +ENABLE_SENSITIVE_DATA=true + +# Optional: Custom service name +OTEL_SERVICE_NAME="contoso-agent" +``` + +### 3. Install Dependencies + +```bash +cd agentic_ai/applications +uv sync +``` + +### 4. Run the Sample + +```bash +# Start MCP server first +cd mcp && uv run python mcp_service.py + +# In another terminal, run the sample +cd agentic_ai/applications +uv run python ../observability/sample_agent_with_tracing.py +``` + +## Usage in Your Code + +### Basic Setup + +```python +from observability import setup_observability, get_tracer + +# Initialize once at application startup +setup_observability( + connection_string="InstrumentationKey=...", # Or set APPLICATIONINSIGHTS_CONNECTION_STRING + service_name="my-agent-app", + enable_live_metrics=True, + enable_sensitive_data=False, # Set True only in dev! +) + +# Use tracer for custom spans +tracer = get_tracer() +with tracer.start_as_current_span("my-operation"): + # Your code here + pass +``` + +### With Agents + +```python +from observability import setup_observability, get_tracer, get_trace_id +from agent_framework import ChatAgent +from opentelemetry.trace import SpanKind + +# Setup observability BEFORE creating agents +setup_observability() + +tracer = get_tracer() + +# Create a parent span for the session +with tracer.start_as_current_span("customer-session", kind=SpanKind.SERVER) as span: + trace_id = get_trace_id() + print(f"Trace ID: {trace_id}") + + # Add custom attributes + span.set_attribute("customer.id", "12345") + span.set_attribute("session.type", "support") + + # Run your agent - all tool calls and LLM invocations are traced automatically + agent = ChatAgent(...) + async for update in agent.run_stream(query, thread=thread): + print(update.text, end="") +``` + +## Viewing Telemetry + +### Azure Monitor Dashboards (Recommended) + +Pre-built dashboards are available via Azure Monitor Workbooks - **no Grafana setup required!** + +| Dashboard | URL | Description | +|-----------|-----|-------------| +| **Agent Overview** | https://aka.ms/amg/dash/af-agent | Agent execution, tool calls, token usage, response times | +| **Workflow Overview** | https://aka.ms/amg/dash/af-workflow | Workflow execution, step timing, fan-out/fan-in | + +**To use:** +1. Click the dashboard link above +2. Select your **Subscription** and **Application Insights** resource from the dropdowns +3. View your live agent telemetry immediately! + +![Agent Framework Dashboard](../docs/media/observability-dashboard.png) + +### Azure Portal (Manual) + +1. Go to Application Insights → **Transaction search** +2. Filter by Trace ID to see the full execution tree +3. Use **Live Metrics** for real-time monitoring + +### KQL Queries + +Use these queries in Application Insights → Logs: + +**View all spans for recent traces:** +```kusto +dependencies +| where operation_Id in (dependencies + | project operation_Id, timestamp + | order by timestamp desc + | summarize operations = make_set(operation_Id), timestamp = max(timestamp) by operation_Id + | order by timestamp desc + | project operation_Id + | take 5) +| evaluate bag_unpack(customDimensions) +| extend tool_call_id = tostring(["gen_ai.tool.call.id"]) +| project-keep timestamp, target, operation_Id, duration, gen_ai* +| order by timestamp asc +``` + +**Token usage by model:** +```kusto +customMetrics +| where name contains "gen_ai.client.token" +| summarize TotalTokens = sum(value) by name, bin(timestamp, 1h) +| render timechart +``` + +## What Gets Traced + +The Agent Framework automatically captures: + +| Span Type | Attributes | +|-----------|------------| +| **Agent Run** | `gen_ai.agent.name`, `gen_ai.agent.id` | +| **LLM Call** | `gen_ai.system`, `gen_ai.request.model`, `gen_ai.usage.input_tokens`, `gen_ai.usage.output_tokens` | +| **Tool Call** | `gen_ai.tool.name`, `gen_ai.tool.call.id`, arguments, results | +| **MCP Tool** | `mcp.server.url`, `mcp.tool.name` | +| **Workflow** | `workflow.name`, `workflow.step`, fan-out/fan-in relationships | + +## Configuration Options + +| Environment Variable | Description | Default | +|---------------------|-------------|---------| +| `APPLICATIONINSIGHTS_CONNECTION_STRING` | App Insights connection string | Required | +| `ENABLE_SENSITIVE_DATA` | Include prompts/responses in traces | `false` | +| `OTEL_SERVICE_NAME` | Service name in telemetry | `agent_framework` | +| `OTEL_SERVICE_VERSION` | Service version | Package version | + +## Troubleshooting + +### No data in Application Insights + +1. Verify connection string is correct +2. Check that `setup_observability()` returns `True` +3. Allow 2-5 minutes for data to appear in the portal +4. Check for firewall rules blocking outbound HTTPS + +### Missing tool calls + +Ensure you're using Agent Framework's MCP integration with `McpServerManager` which has built-in instrumentation. + +### Sensitive data not appearing + +Set `ENABLE_SENSITIVE_DATA=true` or pass `enable_sensitive_data=True` to `setup_observability()`. + +> ⚠️ **Warning**: Never enable sensitive data in production as it may expose PII and confidential information. + +## Related Documentation + +- [Agent Framework Observability](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/observability) +- [Azure Monitor OpenTelemetry](https://learn.microsoft.com/en-us/azure/azure-monitor/app/opentelemetry-enable) +- [OpenTelemetry GenAI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/) diff --git a/agentic_ai/observability/__init__.py b/agentic_ai/observability/__init__.py new file mode 100644 index 000000000..9f5667379 --- /dev/null +++ b/agentic_ai/observability/__init__.py @@ -0,0 +1,33 @@ +# Copyright (c) Microsoft. All rights reserved. + +""" +Observability module for Agent Framework applications. + +This module provides easy-to-use telemetry setup for: +- Traces (spans for agent/tool execution) +- Logs (structured logging with context) +- Metrics (token usage, latency, etc.) + +Quick Start: + from observability import setup_observability, get_tracer + + # Initialize once at startup + setup_observability() + + # Use tracer for custom spans + with get_tracer().start_as_current_span("my-operation"): + # Your code here + pass +""" + +from .setup import ( + setup_observability, + get_tracer, + get_trace_id, +) + +__all__ = [ + "setup_observability", + "get_tracer", + "get_trace_id", +] diff --git a/agentic_ai/observability/sample_agent_with_tracing.py b/agentic_ai/observability/sample_agent_with_tracing.py new file mode 100644 index 000000000..fbcccb5de --- /dev/null +++ b/agentic_ai/observability/sample_agent_with_tracing.py @@ -0,0 +1,158 @@ +# Copyright (c) Microsoft. All rights reserved. + +""" +Sample agent with full observability using Application Insights. + +This demonstrates how to: +1. Configure observability with Application Insights +2. Create custom spans for operations +3. Run agents with full tracing of tool calls and LLM invocations +4. View results in Grafana dashboards + +Usage: + cd agentic_ai/applications + uv run python ../observability/sample_agent_with_tracing.py + +Prerequisites: + - Set APPLICATIONINSIGHTS_CONNECTION_STRING in .env + - MCP server running (cd mcp && uv run python mcp_service.py) + - Azure OpenAI credentials configured + +Grafana Dashboards: + - Agent Overview: https://aka.ms/amg/dash/af-agent + - Workflow Overview: https://aka.ms/amg/dash/af-workflow +""" + +import asyncio +import os +import sys +from pathlib import Path + +# Add parent directories to path +current_dir = Path(__file__).parent +agentic_ai_dir = current_dir.parent +applications_dir = agentic_ai_dir / "applications" +sys.path.insert(0, str(agentic_ai_dir)) +sys.path.insert(0, str(applications_dir)) + +# Load environment variables +from dotenv import load_dotenv +load_dotenv(applications_dir / ".env") + +# Import observability BEFORE creating any agents +from observability import setup_observability, get_tracer, get_trace_id +from opentelemetry.trace import SpanKind + +# Setup observability first +APPINSIGHTS_CONNECTION_STRING = os.environ.get("APPLICATIONINSIGHTS_CONNECTION_STRING") + +if not APPINSIGHTS_CONNECTION_STRING: + print("❌ APPLICATIONINSIGHTS_CONNECTION_STRING not set in environment") + print(" Add it to agentic_ai/applications/.env") + sys.exit(1) + +success = setup_observability( + connection_string=APPINSIGHTS_CONNECTION_STRING, + service_name="contoso-agent-demo", + enable_live_metrics=True, + enable_sensitive_data=True, # Enable to see prompts/responses (dev only!) +) + +if not success: + print("❌ Failed to configure observability") + sys.exit(1) + + +async def run_agent_with_tracing(): + """Run a sample agent with full observability.""" + + from agent_framework import ChatAgent + from agent_framework.mcp import McpServerManager + from agent_framework.openai import AzureOpenAIChatClient + from azure.identity import DefaultAzureCredential + + # Get tracer for custom spans + tracer = get_tracer("contoso-agent-demo") + + # MCP server URL + mcp_url = os.environ.get("MCP_SERVER_URI", "http://localhost:8000/mcp") + + # Azure OpenAI configuration + azure_endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + deployment_name = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o") + + if not azure_endpoint: + print("❌ AZURE_OPENAI_ENDPOINT not set") + return + + print("=" * 60) + print("Agent with Application Insights Observability") + print("=" * 60) + + # Create a parent span for the entire session + with tracer.start_as_current_span("customer-service-session", kind=SpanKind.SERVER) as session_span: + trace_id = get_trace_id() + print(f"\n📊 Trace ID: {trace_id}") + print(f" View in Azure Portal or Grafana after completion") + print() + + # Add custom attributes to the span + session_span.set_attribute("customer.scenario", "billing-inquiry") + session_span.set_attribute("session.type", "demo") + + async with McpServerManager(mcp_url) as mcp_manager: + mcp_tools = await mcp_manager.list_tools() + print(f"📦 Loaded {len(mcp_tools)} MCP tools") + + # Create chat client + chat_client = AzureOpenAIChatClient( + azure_endpoint=azure_endpoint, + azure_deployment=deployment_name, + credential=DefaultAzureCredential(), + ) + + # Create agent + agent = ChatAgent( + chat_client=chat_client, + tools=mcp_tools, + name="CustomerServiceAgent", + instructions="""You are a helpful customer service agent for Contoso Wireless. + Use the available tools to look up customer information, billing details, and data usage. + Be concise and helpful in your responses.""", + id="customer-service-agent", + ) + + # Sample queries to demonstrate observability + queries = [ + "What's the billing summary for customer 1?", + "Show me the data usage for subscription 1 from 2025-01-01 to 2025-01-15", + ] + + thread = agent.get_new_thread() + + for query in queries: + print(f"\n👤 User: {query}") + print(f"🤖 Agent: ", end="") + + # Each query gets its own span + with tracer.start_as_current_span( + "agent-query", + kind=SpanKind.CLIENT + ) as query_span: + query_span.set_attribute("user.query", query) + + async for update in agent.run_stream(query, thread=thread): + if update.text: + print(update.text, end="") + + print() + + print("\n" + "=" * 60) + print("✅ Session complete!") + print(f"📊 View traces at: https://aka.ms/amg/dash/af-agent") + print(f" Filter by Trace ID: {trace_id}") + print("=" * 60) + + +if __name__ == "__main__": + asyncio.run(run_agent_with_tracing()) diff --git a/agentic_ai/observability/setup.py b/agentic_ai/observability/setup.py new file mode 100644 index 000000000..d91e6ab17 --- /dev/null +++ b/agentic_ai/observability/setup.py @@ -0,0 +1,104 @@ +# Copyright (c) Microsoft. All rights reserved. + +""" +Observability setup for Agent Framework applications with Application Insights. + +This module configures OpenTelemetry to send traces, logs, and metrics to +Azure Application Insights, enabling full observability of agent executions. + +Azure Monitor Dashboards (no Grafana required): + - Agent Overview: https://aka.ms/amg/dash/af-agent + - Workflow Overview: https://aka.ms/amg/dash/af-workflow + +Usage: + 1. Set APPLICATIONINSIGHTS_CONNECTION_STRING in your .env + 2. Call setup_observability() once at app startup + 3. All Agent Framework traces are captured automatically +""" + +import os +import logging +from typing import Optional + +logger = logging.getLogger(__name__) + +# Track initialization state +_initialized = False + + +def setup_observability( + connection_string: Optional[str] = None, + service_name: str = "contoso-agent", + enable_live_metrics: bool = True, + enable_sensitive_data: bool = False, +) -> bool: + """ + Configure Application Insights for Agent Framework observability. + + This follows the pattern from agent-framework/python/samples/getting_started/observability/. + + Args: + connection_string: App Insights connection string (or set APPLICATIONINSIGHTS_CONNECTION_STRING). + service_name: Service name shown in App Insights. + enable_live_metrics: Enable Live Metrics stream. + enable_sensitive_data: Include prompts/responses in traces (dev only!). + + Returns: + True if setup succeeded, False otherwise. + """ + global _initialized + + if _initialized: + return True + + # Get connection string from parameter or environment + conn_str = connection_string or os.environ.get("APPLICATIONINSIGHTS_CONNECTION_STRING") + + if not conn_str: + logger.debug("No APPLICATIONINSIGHTS_CONNECTION_STRING - observability disabled") + return False + + try: + from azure.monitor.opentelemetry import configure_azure_monitor + from agent_framework.observability import create_resource, enable_instrumentation + + # Set service name via standard env var + os.environ.setdefault("OTEL_SERVICE_NAME", service_name) + + # Configure Azure Monitor (same pattern as agent-framework samples) + configure_azure_monitor( + connection_string=conn_str, + resource=create_resource(), + enable_live_metrics=enable_live_metrics, + ) + + # Enable Agent Framework instrumentation + enable_instrumentation(enable_sensitive_data=enable_sensitive_data) + + _initialized = True + logger.info(f"✅ Application Insights observability enabled (service: {service_name})") + return True + + except ImportError as e: + logger.warning(f"Observability dependencies not installed: {e}") + return False + except Exception as e: + logger.warning(f"Failed to configure observability: {e}") + return False + + +def get_tracer(name: str = "contoso-agent"): + """Get an OpenTelemetry tracer for creating custom spans.""" + from agent_framework.observability import get_tracer as af_get_tracer + return af_get_tracer(name) + + +def get_trace_id() -> Optional[str]: + """Get the current trace ID for correlation.""" + from opentelemetry import trace + from opentelemetry.trace.span import format_trace_id + + current_span = trace.get_current_span() + if current_span and current_span.get_span_context().is_valid: + return format_trace_id(current_span.get_span_context().trace_id) + return None diff --git a/agentic_ai/evaluations/telemetry.py b/agentic_ai/observability/telemetry.py similarity index 100% rename from agentic_ai/evaluations/telemetry.py rename to agentic_ai/observability/telemetry.py diff --git a/agentic_ai/scenarios/durable_agent/README.md b/agentic_ai/scenarios/durable_agent/README.md deleted file mode 100644 index a5cb6820c..000000000 --- a/agentic_ai/scenarios/durable_agent/README.md +++ /dev/null @@ -1,110 +0,0 @@ -# Durable-Agent Demo 🚀 - -> An experiment in making **AI agents durable** – i.e. able to -> preserve state, survive crashes / restarts and handle long-running -> workflows without blocking. - ---- - -## 1. Why “durable” agents? - -Normal conversational agents keep all their short-term memory **in RAM**. -If the process dies, the whole context is lost. Durable agents, in contrast, are designed to: - -1. **Persist state** – every turn they write their internal state to an external store (Cosmos DB in Azure or an in-memory dict for local runs). -2. **Resume seamlessly** – on the next request they reload that state and continue the conversation exactly where they left off. -3. **Survive long-running work** – they can kick off background jobs or remote orchestrations (Azure Durable Functions, workflows, etc.), go completely idle, and later integrate the result back into the conversation once it is available. - -These three abilities together make a durable agent *resilient*, *tolerant to infra hiccups*, and *suitable for complex, multi-step business flows*. - ---- - -## 2. High-level architecture - -```mermaid -flowchart TD - A["Streamlit UI"] -- REST --> B["FastAPI Backend"] - subgraph Backend - B --> C["Durable Agent
Round-Robin
Group Chat"] - C -- "load / save" --> D["State Store
CosmosDB/dict"] - C -- "calls" --> E["MCP & Local Tools"] - C -- "schedules" --> F["Background Worker
(thread/Durable Fn.)"] - F -- "update" --> D - end -``` - -## 3. Sequence of a long-running operation -```mermaid -sequenceDiagram - participant U as User (UI) - participant BE as FastAPI - participant AG as Durable Agent - participant BG as Background Job - participant SS as State Store - - U->>BE: "Activate line …" - BE->>AG: chat(task) - AG->>AG: tool call activate_new_line - AG->>BG: spawn thread & return - AG->>SS: save state - AG->>BE: "Task scheduled, please wait…" - BE->>U: immediate reply - - Note over BG: ~long running task - BG-->>SS: append result message - - U->>BE: "Is it done?" - BE->>AG: chat(next turn) - AG->>SS: load state (+result) - AG-->>BE: "✅ Done, line is active." - BE-->>U: final reply -``` -## 4. Current Demo – What is Implemented? - -| Capability | Status | Notes | -|---------------------------------------|:------:|--------------------------------------------------------------------------------------------------------| -| Resilient state persistence | ✅ | Cosmos DB or in-mem dict | -| Agent re-hydration | ✅ | Agent loads TeamState on every request | -| Long-running tool scheduling | ✅ | Simulated via Python threading (20 s sleep) | -| Result injection after completion | ✅ | Worker edits saved state (`FunctionExecutionResultMessage`) | -| Push notifications to UI (SSE) | ♻️ | Optional add-on shown in docs | -| Integration with Azure Durable Fn. | 🟡 | Conceptually supported; not wired-up in repo | -| Full error-handling / retries | 🟡 | Basic happy-path only | - ---- - -## 5. Code Hotspots - -- **agents/base_agent.py** - Abstract helper that reads env-vars and exposes `chat_async`. - -- **agents/durable_agent/loop_agent.py** - - Builds a `RoundRobinGroupChat` with one `AssistantAgent`. - - Registers normal MCP tools and a custom long-running tool `activate_new_line`. - - Spawns a background thread that, after the fake 20 s delay, loads the saved `TeamState`, injects a synthetic `ToolCallRequestEvent` plus matching `ToolCallExecutionEvent`, and rewrites the state. - -- **utils/CosmosDBStateStore** - Thin wrapper around Cosmos container ⇒ “dict-like” API. - -- **backend.py** - FastAPI endpoints: - `/chat`, `/history/{id}`, `/reset_session`. - (Optional) `/events/{id}` for Server-Sent Events push. - ---- - -## 6. Extending the Pattern - -- Swap the background threading code for Azure Durable Functions or Azure Container Apps Jobs – post the final status to the same state store key. -- Support multiple parallel long-running calls by including a unique tool `call_id` per job; each worker injects its own result message. -- Add retry metadata (e.g. “attempt #”, “next ETA”) inside the state so the agent can reason about failures and keep the user informed. -- Implement push UI (SSE / WebSockets) for real-time updates without polling. - ---- - -## 7. Limitations & Next Steps - -- Persisted messages grow indefinitely – add TTL / pruning. -- Background thread approach works only for single-process deployments – use external orchestrators in prod. -- No security model yet (authN / authZ on APIs, encrypt Cosmos). - diff --git a/agentic_ai/scenarios/durable_agent/loop_agent.py b/agentic_ai/scenarios/durable_agent/loop_agent.py deleted file mode 100644 index e383a7c43..000000000 --- a/agentic_ai/scenarios/durable_agent/loop_agent.py +++ /dev/null @@ -1,295 +0,0 @@ -import os -from dotenv import load_dotenv -from typing import Any, Dict -from autogen_agentchat.agents import AssistantAgent -from autogen_agentchat.teams import RoundRobinGroupChat -from autogen_agentchat.conditions import TextMessageTermination -from autogen_core import CancellationToken -from autogen_ext.models.openai import AzureOpenAIChatCompletionClient -from autogen_ext.tools.mcp import StreamableHttpServerParams, mcp_server_tools -from agents.base_agent import BaseAgent -import threading, asyncio, uuid -from datetime import datetime,timezone -from autogen_core.model_context import BufferedChatCompletionContext -import json - -load_dotenv() - -# NEW imports -import asyncio, threading, time -from typing import Any, Dict - - - -# --------------------------------------------------------------------- -# 1. User-visible tool (LLM will call this) -# --------------------------------------------------------------------- -async def activate_new_line(customer_id: str, phone_number: str) -> str: - """ - Handle the activation of a new line for a customer. - - The operation is long-running (a few minutes). - Immediately acknowledge that the task was scheduled; the customer will be - notified once activation is complete. - """ - # Body never executed – see wrapper registration further below. -# ─────────────────────────────────────────────────────────────────────── -# 2) internal helper – does the real scheduling -# ─────────────────────────────────────────────────────────────────────── -# --------------------------------------------------------------------- -# 2. Internal helper, does the real scheduling -# --------------------------------------------------------------------- -async def _activate_new_line_impl( - customer_id: str, - phone_number: str, - *, - __session_id__: str, - __state_store__: Dict[str, Any], -) -> str: - """ - Internal helper invoked by wrapper; identical semantics but with context. - """ - - # ── background job that will run AFTER 20 s ────────────────────── - - - async def _post_completion() -> None: - await asyncio.sleep(25) # 1️⃣ simulate work (shortened for testing) - print(f"🔔 Background completion starting for session {__session_id__}") - - # 2️⃣ Load the persisted TeamState dict - team_state: dict[str, Any] | None = __state_store__.get(__session_id__) - if team_state is None: - return # session was wiped - - # 3️⃣ Compute helper refs - ai_ctx = team_state["agent_states"]["ai_assistant"]["agent_state"]["llm_context"]["messages"] - thread = team_state["agent_states"]["RoundRobinGroupChatManager"]["message_thread"] - - # 4️⃣ Build a NEW tool-call id & arguments - new_call_id = f"call_{uuid.uuid4().hex}" - arguments_json = json.dumps( - {"customer_id": customer_id, "phone_number": phone_number} - ) - - # 5️⃣ Tool-CALL message (AssistantMessage / ToolCallRequestEvent) - call_msg_assistant = { - "content": [ - { - "id": new_call_id, - "arguments": arguments_json, - "name": "activate_new_line_result_update", - } - ], - "thought": None, - "source": "ai_assistant", - "type": "AssistantMessage", - } - call_msg_thread = { - "id": str(uuid.uuid4()), - "source": "ai_assistant", - "models_usage": None, - "metadata": {}, - "created_at": datetime.utcnow().isoformat(timespec="milliseconds") + "Z", - "content": call_msg_assistant["content"], - "type": "ToolCallRequestEvent", - } - - # 6️⃣ EXECUTION / result message - exec_payload = { - "content": ( - f"✅ Activation complete – customer {customer_id}, " - f"phone {phone_number} is now live." - ), - "name": "activate_new_line_result_update", - "call_id": new_call_id, - "is_error": False, - } - exec_msg_assistant = { - "content": [exec_payload], - "type": "FunctionExecutionResultMessage", - } - exec_msg_thread = { - "id": str(uuid.uuid4()), - "source": "ai_assistant", - "models_usage": None, - "metadata": {}, - "created_at": datetime.utcnow().isoformat(timespec="milliseconds") + "Z", - "content": [exec_payload], - "type": "ToolCallExecutionEvent", - } - - # 7️⃣ Append to assistant LLM context (keeps order) - ai_ctx.extend([call_msg_assistant, exec_msg_assistant]) - - # 8️⃣ Append to group-chat message thread - thread.extend([call_msg_thread, exec_msg_thread]) - - # 9️⃣ Persist updated state - __state_store__[__session_id__] = team_state - - # 🔟 Try to live-push these synthetic events to any websocket clients - print(f"🔔 Attempting to broadcast completion events for session {__session_id__}") - try: - # Access MANAGER through builtins (set by backend) - import builtins - MANAGER = getattr(builtins, 'GLOBAL_WS_MANAGER', None) - if MANAGER: - print(f"Found MANAGER via builtins") - else: - print("No GLOBAL_WS_MANAGER found in builtins") - except Exception as e: - print(f"Error accessing builtins: {e}") - MANAGER = None - - if MANAGER: - print(f"Broadcasting completion events to {len(MANAGER.sessions.get(__session_id__, []))} clients") - # Mirror Autogen stream shapes used in serialize_autogen_event - try: - await MANAGER.broadcast(__session_id__, { - "type": "tool_call", - "calls": [{ - "name": "activate_new_line_result_update", - "arguments": {"customer_id": customer_id, "phone_number": phone_number} - }] - }) - await MANAGER.broadcast(__session_id__, { - "type": "tool_result", - "results": [{ - "name": "activate_new_line_result_update", - "is_error": False, - "content": f"✅ Activation complete – customer {customer_id}, phone {phone_number} is now live." - }] - }) - await MANAGER.broadcast(__session_id__, { - "type": "message", - "role": "assistant", - "content": f"Activation finished for {phone_number}." - }) - print("Successfully broadcast all completion events") - except Exception as e: - print(f"Error broadcasting: {e}") - else: - print("No MANAGER found - background events will not be pushed to UI") - # Schedule background coroutine on current loop (preferred over new loop in thread) - try: - loop = asyncio.get_running_loop() - loop.create_task(_post_completion()) - except RuntimeError: - # Fallback (should not normally happen inside async context) - threading.Thread(target=lambda: asyncio.run(_post_completion()), daemon=True).start() - - # Immediate (first) response shown to the user - return ( - "🔔 Background task scheduled. " - "Activation will take a few minutes; you will be notified once it is done." - ) -# --------------------------------------------------------------------------- -class Agent(BaseAgent): - def __init__(self, state_store, session_id, access_token: str | None = None) -> None: - super().__init__(state_store, session_id) - self.loop_agent = None - self._initialized = False - self._access_token = access_token - - async def _setup_loop_agent(self) -> None: - """Initialize the assistant and tools once.""" - if self._initialized: - return - - headers = {"Content-Type": "application/json"} - if self._access_token: - headers["Authorization"] = f"Bearer {self._access_token}" - - server_params = StreamableHttpServerParams( - url=self.mcp_server_uri, - headers=headers, - timeout=30 - ) - - # Fetch tools (async) - tools = await mcp_server_tools(server_params) - - async def _activate_wrapper(customer_id: str, phone_number: str) -> str: - # Call internal helper with conversation context - return await _activate_new_line_impl( - customer_id, - phone_number, - __session_id__=self.session_id, - __state_store__=self.state_store, - ) - - # Copy metadata so AssistantAgent sees the right signature/docs - _activate_wrapper.__name__ = activate_new_line.__name__ - _activate_wrapper.__doc__ = activate_new_line.__doc__ - _activate_wrapper.__annotations__ = activate_new_line.__annotations__ - - tools.append(_activate_wrapper) # AFTER tools = await mcp_server_tools() - # Set up the OpenAI/Azure model client - model_client = AzureOpenAIChatCompletionClient( - api_key=self.azure_openai_key, - azure_endpoint=self.azure_openai_endpoint, - api_version=self.api_version, - azure_deployment=self.azure_deployment, - model=self.openai_model_name, - ) - - # Set up the assistant agent - model_context = BufferedChatCompletionContext(buffer_size=10) - agent = AssistantAgent( - name="ai_assistant", - model_client=model_client, - model_context=model_context, - tools=tools, - system_message=( - "You are a helpful assistant. You can use multiple tools to find information and answer questions. " - "Review the tools available to you and use them as needed. You can also ask clarifying questions if " - "the user is not clear." - ) - ) - - # Set the termination condition: stop when agent answers as itself - termination_condition = TextMessageTermination("ai_assistant") - - self.loop_agent = RoundRobinGroupChat( - [agent], - termination_condition=termination_condition, - ) - - if self.state: - await self.loop_agent.load_state(self.state) - self._initialized = True - - async def chat_async(self, prompt: str) -> str: - """Ensure agent/tools are ready and process the prompt.""" - await self._setup_loop_agent() - - response = await self.loop_agent.run(task=prompt, cancellation_token=CancellationToken()) - assistant_response = response.messages[-1].content - - messages = [ - {"role": "user", "content": prompt}, - {"role": "assistant", "content": assistant_response} - ] - self.append_to_chat_history(messages) - - # Update/store latest agent state - new_state = await self.loop_agent.save_state() - print(f"Updated state for session {self.session_id}: {new_state}") - self._setstate(new_state) - - return assistant_response - async def chat_stream(self, prompt: str): - """ - Async generator that yields Autogen streaming events while processing prompt. - Backend will consume this and forward to frontend. - """ - await self._setup_loop_agent() - stream = self.loop_agent.run_stream(task=prompt, cancellation_token=CancellationToken()) - - async for event in stream: - yield event - - # After run finishes, persist state - new_state = await self.loop_agent.save_state() - self._setstate(new_state) \ No newline at end of file diff --git a/agentic_ai/scenarios/progress_update/chainlit.md b/agentic_ai/scenarios/progress_update/chainlit.md deleted file mode 100644 index 4507ac467..000000000 --- a/agentic_ai/scenarios/progress_update/chainlit.md +++ /dev/null @@ -1,14 +0,0 @@ -# Welcome to Chainlit! 🚀🤖 - -Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs. - -## Useful Links 🔗 - -- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚 -- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! 💬 - -We can't wait to see what you create with Chainlit! Happy coding! 💻😊 - -## Welcome screen - -To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty. diff --git a/agentic_ai/scenarios/progress_update/frontend.py b/agentic_ai/scenarios/progress_update/frontend.py deleted file mode 100644 index 9df4dfc16..000000000 --- a/agentic_ai/scenarios/progress_update/frontend.py +++ /dev/null @@ -1,87 +0,0 @@ -# ui/chainlit_backend_stream.py -import os -import uuid -import json -import asyncio -import chainlit as cl -import websockets - -# Backend URL, e.g., ws://localhost:7000 -BACKEND_URL = os.getenv("BACKEND_URL", "http://localhost:7000") -# Convert to ws:// if http:// given -if BACKEND_URL.startswith("http://"): - WS_BASE = "ws://" + BACKEND_URL[len("http://"):] -elif BACKEND_URL.startswith("https://"): - WS_BASE = "wss://" + BACKEND_URL[len("https://"):] -else: - WS_BASE = BACKEND_URL - -WS_CHAT_URL = f"{WS_BASE}/ws/chat" - -@cl.on_chat_start -async def on_chat_start(): - # per-user session id - session_id = str(uuid.uuid4()) - cl.user_session.set("session_id", session_id) - await cl.Message(f"Session: {session_id}\nHow can I help you?").send() - -@cl.on_message -async def on_message(message: cl.Message): - session_id = cl.user_session.get("session_id") - - # Prepare messages - progress_msg = cl.Message(content="") - assistant_msg = cl.Message(content="") - await progress_msg.send() # create placeholder - await assistant_msg.send() - - # Optional: If your backend WS requires token, set it here - # headers = [("Authorization", f"Bearer {YOUR_TOKEN}")] - headers = None - - async def send_prompt(): - async with websockets.connect(WS_CHAT_URL, additional_headers=headers) as ws: - payload = { - "session_id": session_id, - "prompt": message.content, - # "access_token": "...", # include if your backend forwards to MCP - } - await ws.send(json.dumps(payload)) - - while True: - try: - raw = await ws.recv() - except websockets.ConnectionClosed: - break - - try: - data = json.loads(raw) - except Exception: - continue - - typ = data.get("type") - if typ == "progress": - # side-channel MCP tool progress - line = f"[{data.get('percent', 0)}%] {data.get('message', '')}\n" - await progress_msg.stream_token(line) - elif typ == "token": - await assistant_msg.stream_token(data.get("content", "")) - elif typ == "message": - await assistant_msg.stream_token("\n" + data.get("content", "")) - elif typ == "final": - # Final assistant message - content = data.get("content", "") - if content: - await assistant_msg.stream_token("\n" + content) - elif typ == "done": - # finish - break - elif typ == "error": - await assistant_msg.stream_token(f"\n[Error] {data.get('message','')}") - break - - await send_prompt() - - # finalize messages - await progress_msg.update() - await assistant_msg.update() \ No newline at end of file diff --git a/agentic_ai/scenarios/progress_update/loop_agent_progress.py b/agentic_ai/scenarios/progress_update/loop_agent_progress.py deleted file mode 100644 index 37824fa1e..000000000 --- a/agentic_ai/scenarios/progress_update/loop_agent_progress.py +++ /dev/null @@ -1,231 +0,0 @@ -# agents/autogen/single_agent/loop_agent.py -import os -import asyncio -from typing import Any, Callable, Awaitable, Optional, Mapping, List - -from dotenv import load_dotenv - -from autogen_agentchat.agents import AssistantAgent -from autogen_agentchat.teams import RoundRobinGroupChat -from autogen_agentchat.conditions import TextMessageTermination -from autogen_core import CancellationToken -from autogen_core.tools import BaseTool -from autogen_core.utils import schema_to_pydantic_model -from pydantic import BaseModel - -from autogen_ext.models.openai import AzureOpenAIChatCompletionClient - -from fastmcp.client import Client -from fastmcp.client.transports import StreamableHttpTransport - -from agents.base_agent import BaseAgent -import mcp -from fastmcp.exceptions import ToolError - - - -load_dotenv() - - -# A simple Pydantic model for the return value (BaseTool requires a BaseModel return type) -class ToolTextResult(BaseModel): - text: str - -ProgressSink = Callable[[dict], Awaitable[None]] - -class MCPProgressTool(BaseTool[BaseModel, ToolTextResult]): - """ - Wrap a remote MCP tool so Autogen sees it as a local tool, while forwarding progress updates. - """ - - def __init__( - self, - client: Client, - mcp_tool: mcp.types.Tool, - progress_sink: Optional[ProgressSink] = None, - ) -> None: - # Build a Pydantic args model from the MCP tool's JSON schema - args_model = schema_to_pydantic_model(mcp_tool.inputSchema) - super().__init__( - args_type=args_model, - return_type=ToolTextResult, - name=mcp_tool.name, - description=mcp_tool.description or "", - strict=False, # set True if you want to enforce no extra args/defaults - ) - self._client = client - self._tool_name = mcp_tool.name - self._progress_sink = progress_sink - - async def run(self, args: BaseModel, cancellation_token: CancellationToken) -> ToolTextResult: - # Serialize args excluding unset values so we only send what's provided - kwargs: Mapping[str, Any] = args.model_dump(exclude_unset=True) - - async def progress_cb(progress: float, total: float | None, message: str | None): - if not self._progress_sink: - return - try: - pct = int((progress / total) * 100) if total else int(progress) - except Exception: - pct = int(progress) - await self._progress_sink({ - "type": "progress", - "tool": self._tool_name, - "percent": pct, - "message": message or "", - }) - - # Use a fresh session for each tool call to avoid cross-call state pollution - async with self._client.new() as c: - call_coro = c.call_tool_mcp( - name=self._tool_name, - arguments=dict(kwargs), - progress_handler=progress_cb, - ) - task = asyncio.create_task(call_coro) - # If CancellationToken exposes a way to bind, hook it here. Otherwise, just check state: - if cancellation_token.is_cancelled(): - task.cancel() - raise asyncio.CancelledError("Operation cancelled") - - try: - result: mcp.types.CallToolResult = await task - except asyncio.CancelledError: - # Propagate cancellation - raise - - if result.isError: - # Bubble up MCP tool error for Autogen to surface - msg = "" - try: - msg = (result.content[0].text if result.content else "Tool error") - except Exception: - msg = "Tool error" - raise ToolError(msg) - - # Aggregate text contents; adjust as needed if you want images/resources - texts: list[str] = [] - for content in result.content: - if isinstance(content, mcp.types.TextContent): - texts.append(content.text) - final_text = "\n".join(texts) if texts else "(no text content)" - return ToolTextResult(text=final_text) - - # Provide a readable string for the tool’s result (what the LLM “sees” in logs/streams) - def return_value_as_string(self, value: Any) -> str: - try: - if isinstance(value, ToolTextResult): - return value.text - except Exception: - pass - return super().return_value_as_string(value) - - - - -class Agent(BaseAgent): - def __init__(self, state_store, session_id, access_token: str | None = None) -> None: - super().__init__(state_store, session_id) - self.loop_agent = None - self._initialized = False - self._access_token = access_token - self._progress_sink: Optional[Callable[[dict], Awaitable[None]]] = None # side-channel sink - - def set_progress_sink(self, sink: Optional[Callable[[dict], Awaitable[None]]]) -> None: - """Install (or remove) a per-call async sink to receive side-channel tool progress events.""" - self._progress_sink = sink - - async def _build_mcp_progress_tools(self, - url: str, - headers: Optional[dict[str, str]] = None, - auth: Optional[str] = None, # "Bearer " or fastmcp.client.auth.BearerAuth - progress_sink: Optional[ProgressSink] = None, - ) -> List[MCPProgressTool]: - """ - Create progress-aware Autogen tools for every remote MCP tool at the given endpoint. - """ - transport = StreamableHttpTransport(url, headers=headers, auth=auth) - - client = Client(transport=transport) - async with client: - tools_resp = await client.list_tools_mcp() - adapters: List[MCPProgressTool] = [] - for mcp_tool in tools_resp.tools: - adapters.append(MCPProgressTool(client, mcp_tool, progress_sink)) - return adapters - - - async def _setup_loop_agent(self) -> None: - """Initialize the assistant and loop agent once, using our progress-aware tools.""" - if self._initialized: - return - - # Build tools with progress support - tools = await self._build_mcp_progress_tools( - url=self.mcp_server_uri, - headers={"Authorization": f"Bearer {self._access_token}"} if self._access_token else None, - progress_sink=self._progress_sink, - ) - - # Set up the OpenAI/Azure model client - model_client = AzureOpenAIChatCompletionClient( - api_key=self.azure_openai_key, - azure_endpoint=self.azure_openai_endpoint, - api_version=self.api_version, - azure_deployment=self.azure_deployment, - model=self.openai_model_name, - ) - - # Set up the assistant agent - agent = AssistantAgent( - name="ai_assistant", - model_client=model_client, - tools=tools, - system_message=( - "You are a helpful assistant. You can use tools to get work done. " - "Provide progress when running long operations." - ), - ) - - termination_condition = TextMessageTermination("ai_assistant") - - self.loop_agent = RoundRobinGroupChat( - [agent], - termination_condition=termination_condition, - ) - - if self.state: - await self.loop_agent.load_state(self.state) - self._initialized = True - - async def chat_async(self, prompt: str) -> str: - """Backwards-compatible single-shot call.""" - await self._setup_loop_agent() - response = await self.loop_agent.run(task=prompt, cancellation_token=CancellationToken()) - assistant_response = response.messages[-1].content - - messages = [ - {"role": "user", "content": prompt}, - {"role": "assistant", "content": assistant_response} - ] - self.append_to_chat_history(messages) - - new_state = await self.loop_agent.save_state() - self._setstate(new_state) - - return assistant_response - - async def chat_stream(self, prompt: str): - """ - Async generator that yields Autogen streaming events while processing prompt. - Backend will consume this and forward to frontend. - """ - await self._setup_loop_agent() - stream = self.loop_agent.run_stream(task=prompt, cancellation_token=CancellationToken()) - - async for event in stream: - yield event - - # After run finishes, persist state - new_state = await self.loop_agent.save_state() - self._setstate(new_state) \ No newline at end of file diff --git a/agentic_ai/workflow/README.md b/agentic_ai/workflow/README.md deleted file mode 100644 index cca59f26b..000000000 --- a/agentic_ai/workflow/README.md +++ /dev/null @@ -1,195 +0,0 @@ -# Workflow Architecture - -The Agent Framework workflow system is a **directed-graph execution engine** modeled after Google's [Pregel](https://research.google/pubs/pub36726/) distributed graph computation model, adapted for orchestrating AI agents, tools, and arbitrary compute steps in a type-safe, checkpointable, and observable manner. - -## Core abstractions - -| Component | Purpose | -|-----------|---------| -| **Executor** (`_executor.py`) | A unit of work with typed handlers that process messages. Can be a class (subclassing `Executor`) or a decorated function (`@executor`). Executors define what input types they accept and what they emit. | -| **Edge / EdgeGroup** (`_edge.py`) | Defines how messages flow between executors. Supports single, fan-out (1→N), fan-in (N→1 aggregation), and switch/case routing patterns. | -| **WorkflowContext** (`_workflow_context.py`) | Injected into each executor handler; provides `send_message()`, `yield_output()`, state persistence APIs (`set_state`, `get_state`, `set_shared_state`). Enforces type safety through generic parameters. | -| **Runner** (`_runner.py`) | Orchestrates execution in synchronized **supersteps**: delivers messages, invokes executors concurrently, drains events, creates checkpoints. Runs until the graph becomes idle (no pending messages). | -| **Workflow** (`_workflow.py`) | The user-facing API that wraps the Runner and provides entry points (`run()`, `run_stream()`, `run_from_checkpoint()`). Built via `WorkflowBuilder`. | - ---- - -## Execution model: Pregel-style supersteps - -### 1. Initialization phase - -- User calls `workflow.run(initial_message)`. -- The starting executor receives the message and runs its handler. -- Handler can emit messages via `ctx.send_message()` or final outputs via `ctx.yield_output()`. -- All emitted messages are queued in the `RunnerContext`. - -### 2. Superstep iteration - -- The Runner **drains** all pending messages from the queue. -- Messages are routed through `EdgeRunner` implementations based on edge topology: - - **SingleEdgeRunner**: Delivers to one target if type and condition match. - - **FanOutEdgeRunner**: Broadcasts to multiple targets or selects a subset dynamically. - - **FanInEdgeRunner**: Buffers messages from multiple sources; delivers aggregated list when all sources have sent. - - **SwitchCaseEdgeRunner**: Evaluates predicates and routes to the first matching case. -- All deliverable messages invoke their target executors **concurrently** (via `asyncio.gather`). -- Each executor processes its messages and may emit new messages or outputs. -- At the end of the superstep: - - Events (outputs, custom events) are streamed to the caller. - - A checkpoint is optionally created (if `CheckpointStorage` is configured). - - The Runner checks if new messages are pending; if yes, starts the next superstep. - -### 3. Convergence / termination - -- The workflow runs until **no messages remain** or the **max iteration limit** is hit. -- Final state is emitted as a `WorkflowStatusEvent`: - - `IDLE`: Clean completion, no pending requests. - - `IDLE_WITH_PENDING_REQUESTS`: Waiting for external input (via `RequestInfoExecutor`). - - `FAILED`: An executor raised an exception. - ---- - -## Message routing and type safety - -- Each executor declares **input types** via handler parameter annotations (`text: str`, `data: MyModel`, etc.). -- `WorkflowContext[T_Out]` declares the **output message type** the executor can emit. -- `WorkflowContext[T_Out, T_W_Out]` adds workflow-level output types (for `yield_output`). -- Edge runners use `executor.can_handle(message_data)` to enforce type compatibility at runtime. -- Routing predicates (`edge.should_route(data)`) and selection functions (`selection_func(data, targets)`) allow dynamic control flow. - ---- - -## State and persistence - -| Layer | Mechanism | -|-------|-----------| -| **Executor-local state** | `ctx.set_state(key, value)` / `ctx.get_state(key)` stores per-executor JSON blobs in the `RunnerContext`. Executors can override `snapshot_state()` / `restore_state()` for custom serialization. | -| **Shared state** | `WorkflowContext.set_shared_state(key, value)` writes to a `SharedState` dictionary visible to all executors. Protected by an async lock to prevent race conditions. | -| **Checkpoints** | After each superstep, the Runner calls `_auto_snapshot_executor_states()`, then serializes:
- Pending messages per executor
- Shared state dictionary
- Executor state snapshots
- Iteration counter / metadata

`CheckpointStorage` (in-memory, file, Redis, Cosmos DB) persists `WorkflowCheckpoint` objects. | -| **Restoration** | `workflow.run_from_checkpoint(checkpoint_id)` rehydrates the full runner context, re-injects shared state, restores iteration count, and validates graph topology (via a hash of the executor/edge structure). | - -Checkpoints are **delta-neutral**: the graph structure itself is not serialized, only the runtime state. You must rebuild the workflow with the same topology before restoring. - ---- - -## Observability and tracing - -- **OpenTelemetry integration**: The workflow creates a root span (`workflow_run`) that encompasses all supersteps. Each executor invocation and edge delivery gets nested spans. -- **Trace context propagation**: Messages carry `trace_contexts` and `source_span_ids` to link spans across async boundaries (following W3C Trace Context). -- **Event streaming**: The Runner emits `WorkflowEvent` subclasses: - - `WorkflowStartedEvent`, `WorkflowStatusEvent` (lifecycle). - - `WorkflowOutputEvent` (from `yield_output`). - - `RequestInfoEvent` (external input requests). - - Custom events via `ctx.add_event()`. -- Events are streamed live via `run_stream()` or collected in `WorkflowRunResult` for batch runs. - ---- - -## Composition patterns - -1. **Nested workflows**: `WorkflowExecutor` wraps a child workflow as an executor. When invoked, it runs the child to completion and processes outputs. -2. **Human-in-the-loop**: `RequestInfoExecutor` emits `RequestInfoEvent`, transitions the workflow to `IDLE_WITH_PENDING_REQUESTS`, and waits for external responses via `send_responses()`. -3. **Multi-agent teams**: `MagenticOrchestratorExecutor` (in `_magentic.py`) wraps multiple agents, manages broadcast/targeted communication, and snapshots each participant's conversation history. - ---- - -## Key design decisions - -- **Type-driven routing**: Edge runners and executors use Python type annotations to enforce contracts at runtime, providing early feedback for wiring errors. -- **Separation of data/control planes**: Executor invocations and message passing happen "under the hood"; only workflow-level events (outputs, requests) are exposed to callers. This keeps the event stream clean and hides internal coordination. -- **Checkpointing by convention**: Executors opt into persistence by implementing `snapshot_state()` or exposing a `state` attribute. The framework handles serialization (including Pydantic models and dataclasses) transparently. -- **Graph immutability**: Once built, workflows are immutable. This enables safe checkpoint restoration and parallel invocations (if you construct separate `Workflow` instances). -- **Concurrency within supersteps**: All deliverable messages in a superstep execute concurrently. This parallelizes work but requires shared state to be protected (via `SharedState`'s async lock). - ---- - -## Validation and safety - -- **Graph validation**: `validate_workflow_graph()` (in `_validation.py`) checks for unreachable executors, missing start nodes, and cycles (for non-cyclic workflows). -- **Concurrent execution guard**: The `Workflow` class prevents multiple `run()` calls on the same instance to avoid state corruption. -- **Max iterations**: Prevents infinite loops by bounding superstep counts (default 100, configurable). -- **Graph signature hashing**: Before restoring a checkpoint, the Runner compares a hash of the workflow topology to the checkpoint metadata to detect structural changes. - ---- - -## Sample execution trace - -``` -User calls workflow.run("hello world") - ↓ -Workflow emits WorkflowStartedEvent, WorkflowStatusEvent(IN_PROGRESS) - ↓ -Executor "upper_case_executor" receives "hello world" - → Handler: to_upper_case(text: str, ctx: WorkflowContext[str]) - → Calls ctx.send_message("HELLO WORLD") - → Message queued - ↓ -Runner drains messages → SingleEdgeRunner delivers to "reverse_text_executor" - ↓ -Executor "reverse_text_executor" receives "HELLO WORLD" - → Handler: reverse_text(text: str, ctx: WorkflowContext[Never, str]) - → Calls ctx.yield_output("DLROW OLLEH") - → WorkflowOutputEvent emitted - ↓ -No more messages → Workflow emits WorkflowStatusEvent(IDLE) - ↓ -workflow.run() returns WorkflowRunResult([WorkflowOutputEvent("DLROW OLLEH"), ...]) -``` - ---- - -## Additional references - -- Full workflow builder API: `WorkflowBuilder` in `_workflow.py`. -- Edge runner implementations: `_edge_runner.py`. -- Checkpoint encoding: `_runner_context.py` (`_encode_checkpoint_value`, `_decode_checkpoint_value`). -- Magentic multi-agent orchestration: `_magentic.py`. - -This architecture balances **expressiveness** (flexible routing, composition), **type safety** (runtime contract enforcement), **observability** (OpenTelemetry spans, event streams), and **durability** (checkpointing for long-running workflows), making it suitable for both simple pipelines and complex multi-agent systems. - ---- - -## Demo: Fraud Detection Workflow - -### 🎯 Production-Ready Implementation - -A comprehensive fraud detection system showcasing enterprise workflow patterns: - -| Feature | Description | -|---------|-------------| -| **Architecture** | Fan-out/fan-in pattern with parallel specialist agents | -| **Human-in-the-Loop** | Analyst review for high-risk cases with checkpointing | -| **Real-Time UI** | React + FastAPI dashboard with WebSocket streaming | -| **MCP Integration** | Filtered tool access for domain-specific analysis | -| **Persistence** | Checkpoint storage for pause/resume workflows | - -**[→ Explore the Fraud Detection Demo](fraud_detection/)** - ---- - -## Quick Start - -**Try the Fraud Detection Demo:** - -```bash -# Terminal 1: Start MCP Server -cd mcp -uv run mcp_service.py - -# Terminal 2: Start Backend -cd agentic_ai/workflow/fraud_detection -uv run --prerelease allow backend.py - -# Terminal 3: Start Frontend -cd agentic_ai/workflow/fraud_detection/ui -npm install && npm run dev - -# Open browser: http://localhost:3000 -``` - ---- - -## Additional Resources - -- **[Human-in-the-Loop Guide](human-in-the-loop.md)** - Comprehensive HITL patterns -- **[Agent Framework GitHub](https://github.com/microsoft/agent-framework)** - Official framework repository -- **[API Documentation](https://github.com/microsoft/agent-framework/tree/main/docs)** - Detailed API reference diff --git a/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md b/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md index 249c67964..fe2ae2649 100644 --- a/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md +++ b/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md @@ -14,7 +14,7 @@ Successfully implemented the Contoso Fraud Detection & Escalation Workflow as sp - BillingChargeExecutor - ✅ FraudRiskAggregatorExecutor (LLM-based fan-in) - ✅ Switch/case routing based on risk score -- ✅ RequestInfoExecutor for human-in-the-loop +- ✅ ReviewGatewayExecutor for human-in-the-loop (uses ctx.request_info API) - ✅ AutoClearExecutor for low-risk cases - ✅ FraudActionExecutor for action execution - ✅ FinalNotificationExecutor for completion @@ -52,14 +52,16 @@ builder.add_switch_case_edge( #### Human-in-the-Loop ```python -# Workflow pauses for analyst review -analyst_review = RequestInfoExecutor( - name="analyst_review", - request_info={ - "type": "fraud_analyst_review", - "instructions": "Review the risk assessment..." - }, -) +# ReviewGatewayExecutor uses ctx.request_info() to pause workflow +class ReviewGatewayExecutor(Executor): + async def handle_assessment(self, ctx, assessment: FraudRiskAssessment): + request = AnalystReviewRequest(...) + await ctx.request_info(request, AnalystDecision) + + @response_handler + async def handle_analyst_response(self, ctx, decision: AnalystDecision): + # Process the analyst decision when workflow resumes + await ctx.send_message(decision) ``` ### 3. **MCP Tool Integration** @@ -113,15 +115,20 @@ The `FraudRiskAggregatorExecutor` uses an LLM agent to: ```python # Workflow state saved at each superstep -checkpoint_storage = InMemoryCheckpointStorage() -workflow = await create_fraud_detection_workflow( +# Using UTF8FileCheckpointStorage wrapper for Windows compatibility +checkpoint_storage = UTF8FileCheckpointStorage("./checkpoints") +workflow = create_fraud_detection_workflow( ..., checkpoint_storage=checkpoint_storage ) -# Can pause/resume indefinitely -checkpoint_id = await workflow.create_checkpoint() -await workflow.restore_from_checkpoint(checkpoint_id) +# Resume from checkpoint after analyst decision +async for event in workflow.run_stream(checkpoint_id=checkpoint_id): + process_event(event) + +# Send responses and continue workflow +async for event in workflow.send_responses_streaming(responses): + process_event(event) ``` ## 📁 Files Created @@ -224,7 +231,7 @@ Learned how to wait for multiple inputs before proceeding (aggregation). Learned conditional routing based on message content and business logic. ### ✅ Human-in-the-Loop -Learned workflow pause/resume with external input using `RequestInfoExecutor`. +Learned workflow pause/resume with external input using `ctx.request_info()` and `@response_handler` decorator. ### ✅ Checkpointing Learned persistent workflow state for long-running processes. @@ -346,16 +353,18 @@ class FraudRiskAggregatorExecutor(Executor): ### Workflow Pause/Resume ```python -# Workflow automatically pauses at RequestInfoExecutor +# Workflow automatically pauses when ctx.request_info() is called async for event in workflow.run_stream(alert): - if hasattr(event, "request_info"): - # Wait for analyst decision - decision = await get_analyst_decision() + if isinstance(event, RequestInfoEvent): + # Workflow is now paused, waiting for analyst decision + request_id = event.request_id + + # ... Get analyst decision from UI ... - # Resume workflow - await workflow.send_responses({decision}) - async for event in workflow.run_stream(alert): - # Continues from where it paused + # Resume workflow with the response + responses = {request_id: analyst_decision} + async for event in workflow.send_responses_streaming(responses): + # @response_handler processes the decision process_event(event) ``` diff --git a/agentic_ai/workflow/fraud_detection/QUICKSTART.md b/agentic_ai/workflow/fraud_detection/QUICKSTART.md index bf998a6bb..213ab2e11 100644 --- a/agentic_ai/workflow/fraud_detection/QUICKSTART.md +++ b/agentic_ai/workflow/fraud_detection/QUICKSTART.md @@ -188,16 +188,16 @@ Additional checks: ### Change Analyst Instructions -**Location**: `create_fraud_detection_workflow()` +**Location**: `ReviewGatewayExecutor` class in `fraud_detection_workflow.py` ```python -analyst_review = RequestInfoExecutor( - name="analyst_review", - request_info={ - "type": "fraud_analyst_review", - "instructions": "YOUR CUSTOM INSTRUCTIONS HERE" - }, -) +# The AnalystReviewRequest dataclass contains the request details +@dataclass +class AnalystReviewRequest: + alert_id: str + customer_id: int + risk_assessment: FraudRiskAssessment + instructions: str = "Review the risk assessment and decide on action" ``` ### Modify Action Logic diff --git a/agentic_ai/workflow/fraud_detection/README.md b/agentic_ai/workflow/fraud_detection/README.md index abadfb4d4..9fa788cb0 100644 --- a/agentic_ai/workflow/fraud_detection/README.md +++ b/agentic_ai/workflow/fraud_detection/README.md @@ -37,9 +37,9 @@ This example demonstrates a comprehensive fraud detection system using the Agent (High Risk ≥0.6) (Low Risk <0.6) ↓ ↓ ┌──────────────┐ ┌──────────────┐ - │ Analyst │ │ Auto Clear │ - │ Review │ │ Executor │ - │ (Human Input)│ └──────┬───────┘ + │ Review │ │ Auto Clear │ + │ Gateway │ │ Executor │ + │(Human Input) │ └──────┬───────┘ └──────┬───────┘ │ ↓ │ ┌──────────────┐ │ @@ -80,10 +80,10 @@ This example demonstrates a comprehensive fraud detection system using the Agent - **Low risk (<0.6)**: Auto-clear ### 5. **Human-in-the-Loop** -- Uses `RequestInfoExecutor` for analyst review -- Workflow pauses and creates checkpoint +- Uses `ctx.request_info()` API for analyst review within `ReviewGatewayExecutor` +- Workflow pauses and creates checkpoint automatically - Analyst provides decision (lock account, refund charges, clear, both) -- Workflow resumes with analyst's decision +- `@response_handler` decorator processes the response when workflow resumes ### 6. **Checkpointing** - Workflow state saved at each superstep @@ -103,8 +103,8 @@ This example demonstrates a comprehensive fraud detection system using the Agent 2. **SuspiciousActivityAlert** → [UsagePattern, Location, Billing] (fan-out) 3. **[UsageAnalysisResult, LocationAnalysisResult, BillingAnalysisResult]** → Aggregator (fan-in) 4. **FraudRiskAssessment** → Switch (risk score check) -5. **FraudRiskAssessment** → AnalystReview OR AutoClear -6. **AnalystDecision** → FraudAction (if high risk) +5. **FraudRiskAssessment** → ReviewGateway OR AutoClear +6. **AnalystDecision** → FraudAction (if high risk, after human review) 7. **ActionResult** → FinalNotification 8. **FinalNotification** → Workflow output @@ -260,7 +260,7 @@ instructions=( 1. **Fan-Out Pattern**: One message → multiple executors 2. **Fan-In Pattern**: Multiple messages → one executor (waits for all) 3. **Switch/Case Routing**: Conditional routing based on message content -4. **Human-in-the-Loop**: Workflow pause/resume with external input +4. **Human-in-the-Loop**: Workflow pause/resume with `ctx.request_info()` and `@response_handler` 5. **Checkpointing**: Persistent workflow state across restarts 6. **MCP Tool Integration**: Domain-specific tool filtering 7. **LLM Agent Executors**: AI-powered decision making @@ -390,6 +390,17 @@ http://localhost:3000 - Check WebSocket connection in DevTools - Verify events are being sent from backend +**Checkpoint Encoding Error (Windows):** + +- If you see `'charmap' codec can't encode character` errors, the backend uses `UTF8FileCheckpointStorage` wrapper to handle Unicode characters in LLM output +- Ensure you're using the latest backend.py which includes this fix + +**Analyst Decision Not Resuming Workflow:** + +- Check that the checkpoint was created successfully (no encoding errors in logs) +- Verify the `request_id` matches between the decision and the pending request +- Check backend logs for `continue_workflow` messages + ## License diff --git a/agentic_ai/workflow/fraud_detection/backend.py b/agentic_ai/workflow/fraud_detection/backend.py index e15446a07..d7dd5497c 100644 --- a/agentic_ai/workflow/fraud_detection/backend.py +++ b/agentic_ai/workflow/fraud_detection/backend.py @@ -28,12 +28,113 @@ WorkflowOutputEvent, WorkflowStatusEvent, RequestInfoEvent, - RequestInfoExecutor, + SuperStepCompletedEvent, + WorkflowCheckpoint, + get_checkpoint_summary, ) from agent_framework.azure import AzureOpenAIChatClient from azure.identity import AzureCliCredential from dotenv import load_dotenv import os +import json +from pathlib import Path +from dataclasses import asdict + + +# ============================================================================ +# UTF-8 Checkpoint Storage Wrapper (fixes Windows encoding issues) +# ============================================================================ + +class UTF8FileCheckpointStorage: + """ + Wrapper around FileCheckpointStorage that ensures UTF-8 encoding. + + This fixes the Windows 'charmap' codec error when LLM output contains + Unicode characters (like combining diacritical marks) that can't be + encoded with the default cp1252 encoding. + """ + + def __init__(self, storage_path: str | Path): + """Initialize the file storage with UTF-8 encoding.""" + self.storage_path = Path(storage_path) + self.storage_path.mkdir(parents=True, exist_ok=True) + logger.info(f"Initialized UTF-8 file checkpoint storage at {self.storage_path}") + + async def save_checkpoint(self, checkpoint: WorkflowCheckpoint) -> str: + """Save a checkpoint with UTF-8 encoding and return its ID.""" + file_path = self.storage_path / f"{checkpoint.checkpoint_id}.json" + checkpoint_dict = asdict(checkpoint) + + def _write_atomic() -> None: + tmp_path = file_path.with_suffix(".json.tmp") + with open(tmp_path, "w", encoding="utf-8") as f: + json.dump(checkpoint_dict, f, indent=2, ensure_ascii=False) + os.replace(tmp_path, file_path) + + await asyncio.to_thread(_write_atomic) + logger.info(f"Saved checkpoint {checkpoint.checkpoint_id} to {file_path}") + return checkpoint.checkpoint_id + + async def load_checkpoint(self, checkpoint_id: str) -> WorkflowCheckpoint | None: + """Load a checkpoint by ID with UTF-8 encoding.""" + file_path = self.storage_path / f"{checkpoint_id}.json" + + if not file_path.exists(): + return None + + def _read() -> dict[str, Any]: + with open(file_path, encoding="utf-8") as f: + return json.load(f) + + checkpoint_dict = await asyncio.to_thread(_read) + checkpoint = WorkflowCheckpoint(**checkpoint_dict) + logger.info(f"Loaded checkpoint {checkpoint_id} from {file_path}") + return checkpoint + + async def list_checkpoint_ids(self, workflow_id: str | None = None) -> list[str]: + """List checkpoint IDs with UTF-8 encoding.""" + def _list_ids() -> list[str]: + checkpoint_ids: list[str] = [] + for file_path in self.storage_path.glob("*.json"): + try: + with open(file_path, encoding="utf-8") as f: + data = json.load(f) + if workflow_id is None or data.get("workflow_id") == workflow_id: + checkpoint_ids.append(data.get("checkpoint_id", file_path.stem)) + except Exception as e: + logger.warning(f"Failed to read checkpoint file {file_path}: {e}") + return checkpoint_ids + + return await asyncio.to_thread(_list_ids) + + async def list_checkpoints(self, workflow_id: str | None = None) -> list[WorkflowCheckpoint]: + """List checkpoint objects with UTF-8 encoding.""" + def _list_checkpoints() -> list[WorkflowCheckpoint]: + checkpoints: list[WorkflowCheckpoint] = [] + for file_path in self.storage_path.glob("*.json"): + try: + with open(file_path, encoding="utf-8") as f: + data = json.load(f) + if workflow_id is None or data.get("workflow_id") == workflow_id: + checkpoints.append(WorkflowCheckpoint.from_dict(data)) + except Exception as e: + logger.warning(f"Failed to read checkpoint file {file_path}: {e}") + return checkpoints + + return await asyncio.to_thread(_list_checkpoints) + + async def delete_checkpoint(self, checkpoint_id: str) -> bool: + """Delete a checkpoint by ID.""" + file_path = self.storage_path / f"{checkpoint_id}.json" + + def _delete() -> bool: + if file_path.exists(): + file_path.unlink() + logger.info(f"Deleted checkpoint {checkpoint_id} from {file_path}") + return True + return False + + return await asyncio.to_thread(_delete) # Load environment variables load_dotenv() @@ -130,7 +231,7 @@ class WorkflowStatus(BaseModel): # Pre-initialized resources (created once on startup) mcp_tool: MCPStreamableHTTPTool | None = None chat_client: AzureOpenAIChatClient | None = None -checkpoint_storage: FileCheckpointStorage | None = None +checkpoint_storage: UTF8FileCheckpointStorage | None = None # ============================================================================ @@ -155,14 +256,18 @@ def disconnect(self, websocket: WebSocket): async def broadcast(self, message: dict): """Broadcast message to all connected clients.""" + msg_type = message.get('type', message.get('event_type', 'unknown')) + logger.info(f"[BROADCAST] Sending message type={msg_type} to {len(self.active_connections)} connections") + if not self.active_connections: - logger.warning(f"No active WebSocket connections to broadcast to. Message type: {message.get('type', 'unknown')}") + logger.warning(f"No active WebSocket connections to broadcast to. Message type: {msg_type}") return disconnected = [] for connection in self.active_connections: try: await connection.send_json(message) + logger.info(f"[BROADCAST] Successfully sent {msg_type}") except Exception as e: logger.error(f"Error sending to WebSocket: {e}") disconnected.append(connection) @@ -314,7 +419,9 @@ async def _lookup() -> tuple[str | None, int | None]: for checkpoint in checkpoints_sorted: try: - pending_requests = RequestInfoExecutor.pending_requests_from_checkpoint(checkpoint) + # Use the new get_checkpoint_summary API instead of RequestInfoExecutor + summary = get_checkpoint_summary(checkpoint) + pending_events = summary.pending_request_info_events except Exception as exc: # pragma: no cover - defensive logging logger.debug( "Unable to inspect checkpoint %s for alert %s: %s", @@ -324,9 +431,9 @@ async def _lookup() -> tuple[str | None, int | None]: ) continue - for pending in pending_requests: + for pending in pending_events: if pending.request_id == request_id: - return checkpoint.checkpoint_id, pending.iteration + return checkpoint.checkpoint_id, checkpoint.iteration_count return None, None @@ -626,52 +733,78 @@ async def send_progress_updates(): # Small delay to ensure WebSocket connection is fully established await asyncio.sleep(0.1) + # Track if we've seen a RequestInfoEvent (workflow is awaiting decision) + seen_request_info = False + request_info_event_data = None + # Run workflow and stream events async for event in workflow.run_stream(alert): + logger.info(f"[DEBUG] Received event: {type(event).__name__}") await process_event(alert_id, event) # Check for human-in-the-loop request if isinstance(event, RequestInfoEvent): + logger.info(f"[DEBUG] RequestInfoEvent detected! request_id={event.request_id}, source={event.source_executor_id}") request_payload = _serialize_analyst_request(event) - checkpoint_id, checkpoint_iteration = await _resolve_checkpoint_for_request( - alert_id, - event.request_id, - ) + logger.info(f"[DEBUG] Serialized request payload keys: {list(request_payload.keys())}") timestamp = datetime.now().isoformat() + # Store the pending decision immediately - checkpoint will be resolved later pending_decisions[alert_id] = { **request_payload, "timestamp": timestamp, "source_executor_id": event.source_executor_id, - "checkpoint_id": checkpoint_id, - "checkpoint_iteration": checkpoint_iteration, + "checkpoint_id": None, # Will be resolved after superstep completes + "checkpoint_iteration": None, } pending_request_events[alert_id] = event active_workflows[alert_id]["status"] = "awaiting_decision" - active_workflows[alert_id]["pending_checkpoint_id"] = checkpoint_id - if checkpoint_id: - active_workflows[alert_id]["last_checkpoint_id"] = checkpoint_id - else: - logger.warning( - "No checkpoint recorded for alert %s request %s; resume will be unavailable until one is created.", - alert_id, - event.request_id, - ) + # Broadcast decision_required immediately - don't wait for checkpoint await manager.broadcast( { "type": "decision_required", "alert_id": alert_id, "request_id": event.request_id, "data": request_payload, - "checkpoint_id": checkpoint_id, - "checkpoint_iteration": checkpoint_iteration, + "checkpoint_id": None, # Will be resolved after superstep completes + "checkpoint_iteration": None, "timestamp": timestamp, } ) + logger.info(f"[DEBUG] Broadcast decision_required for {alert_id}") logger.info(f"Workflow {alert_id} awaiting analyst decision") + + # Mark that we've seen the request - continue processing to get checkpoint + seen_request_info = True + request_info_event_data = event + # DON'T return here - continue to let the superstep complete and checkpoint be created + + # After a superstep completes following a RequestInfoEvent, resolve the checkpoint + elif seen_request_info and isinstance(event, SuperStepCompletedEvent): + logger.info(f"[DEBUG] SuperStepCompleted after RequestInfoEvent - resolving checkpoint") + checkpoint_id, checkpoint_iteration = await _resolve_checkpoint_for_request( + alert_id, + request_info_event_data.request_id, + ) + logger.info(f"[DEBUG] Resolved checkpoint: id={checkpoint_id}, iteration={checkpoint_iteration}") + + # Update stored values with resolved checkpoint + if checkpoint_id: + pending_decisions[alert_id]["checkpoint_id"] = checkpoint_id + pending_decisions[alert_id]["checkpoint_iteration"] = checkpoint_iteration + active_workflows[alert_id]["pending_checkpoint_id"] = checkpoint_id + active_workflows[alert_id]["last_checkpoint_id"] = checkpoint_id + else: + logger.warning( + "No checkpoint recorded for alert %s request %s; resume will be unavailable until one is created.", + alert_id, + request_info_event_data.request_id, + ) + + # Now we can exit - workflow is paused awaiting decision return # If we exit the loop naturally, mark as completed @@ -759,44 +892,49 @@ async def continue_workflow(alert_id: str, responses: dict[str, Any], checkpoint workflow_state.setdefault("workflow_id", workflow.id) workflow_state["status"] = "running" - # Debug: Check what's in the checkpoint + # Debug: Check what's in the checkpoint using get_checkpoint_summary checkpoint = await checkpoint_storage.load_checkpoint(effective_checkpoint_id) if checkpoint: - logger.info(f"Checkpoint has {len(checkpoint.executor_states)} executor states") - analyst_state = checkpoint.executor_states.get("analyst_review", {}) - logger.info(f"analyst_review state keys: {list(analyst_state.keys()) if isinstance(analyst_state, dict) else 'not a dict'}") - if isinstance(analyst_state, dict): - shared_state_key = RequestInfoExecutor._PENDING_SHARED_STATE_KEY - pending_requests = checkpoint.shared_state.get(shared_state_key, {}) - logger.info(f"Pending requests in shared state: {list(pending_requests.keys())}") + summary = get_checkpoint_summary(checkpoint) + logger.info(f"Checkpoint status: {summary.status}") + logger.info(f"Pending request events: {len(summary.pending_request_info_events)}") + for pending in summary.pending_request_info_events: + logger.info(f" - Request ID: {pending.request_id}, Source: {pending.source_executor_id}") - logger.info(f"Starting run_stream_from_checkpoint with responses keys: {list(responses.keys())}") + logger.info(f"Starting workflow resume with responses keys: {list(responses.keys())}") logger.info(f"Checkpoint ID: {effective_checkpoint_id}") - # Immediately broadcast analyst_review completion since the framework may not emit it - # The RequestInfoExecutor was waiting, and when we provide the response, it completes + # Immediately broadcast review_gateway completion since the framework may not emit it + # The executor using ctx.request_info was waiting, and when we provide the response, it completes # but this completion event is not always emitted in the event stream during resume await manager.broadcast({ "alert_id": alert_id, "type": "ExecutorCompletedEvent", "event_type": "executor_completed", - "executor_id": "analyst_review", + "executor_id": "review_gateway", "timestamp": datetime.now().isoformat(), }) - logger.info("Broadcast analyst_review completion event") + logger.info("Broadcast review_gateway completion event") # Track request info executors that completed during resume completed_request_executors = set() - async for event in workflow.run_stream_from_checkpoint( - effective_checkpoint_id, + # First, restore from checkpoint + logger.info(f"Restoring workflow from checkpoint {effective_checkpoint_id}") + async for event in workflow.run_stream( + checkpoint_id=effective_checkpoint_id, checkpoint_storage=checkpoint_storage, - responses=responses, ): + logger.info(f"Restore event received: {type(event).__name__}") + await process_event(alert_id, event) + + # Now send the responses to continue the workflow + logger.info(f"Sending responses to continue workflow: {list(responses.keys())}") + async for event in workflow.send_responses_streaming(responses): logger.info(f"Event received: {type(event).__name__}") - # Track when analyst_review completes so we can re-broadcast at the end - if isinstance(event, ExecutorCompletedEvent) and event.executor_id == "analyst_review": + # Track when review_gateway completes so we can re-broadcast at the end + if isinstance(event, ExecutorCompletedEvent) and event.executor_id == "review_gateway": completed_request_executors.add(event.executor_id) await process_event(alert_id, event) @@ -979,11 +1117,11 @@ async def startup_event(): chat_client = AzureOpenAIChatClient(credential=AzureCliCredential(), deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT")) logger.info("✓ Azure OpenAI client initialized") - # Initialize checkpoint storage + # Initialize checkpoint storage with UTF-8 encoding wrapper import pathlib checkpoint_dir = pathlib.Path("./checkpoints") checkpoint_dir.mkdir(parents=True, exist_ok=True) - checkpoint_storage = FileCheckpointStorage(str(checkpoint_dir)) + checkpoint_storage = UTF8FileCheckpointStorage(str(checkpoint_dir)) logger.info(f"✓ Checkpoint storage initialized at {checkpoint_dir.absolute()}") logger.info("Backend ready! 🚀") diff --git a/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py b/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py index 8b6b8b0b1..da36abf1e 100644 --- a/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py +++ b/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py @@ -16,12 +16,13 @@ 3. Fan-In to FraudRiskAggregatorExecutor (LLM-based agent): - Produces FraudRiskScore and recommended action (lock account, refund charges, ignore) 4. SwitchCaseEdgeRunner: - - If risk score ≥ threshold → route to RequestInfoExecutor for human fraud analyst review + - If risk score ≥ threshold → route to ReviewGatewayExecutor for human fraud analyst review - Else → route to AutoClearExecutor -5. RequestInfoExecutor → sends "Fraud Case Review Request" to analyst with full context -6. Workflow pauses — checkpoint saved +5. ReviewGatewayExecutor → calls ctx.request_info() to request analyst decision, workflow pauses +6. Checkpoint saved — workflow awaits analyst response 7. Analyst decides (approve lock/refund or clear) 8. Workflow resumes: + - ReviewGatewayExecutor's @response_handler processes analyst decision - FraudActionExecutor → performs chosen action (e.g., lock account, reverse charges) 9. FinalNotificationExecutor → informs customer and logs audit trail @@ -30,7 +31,7 @@ - Fan-out pattern to multiple specialist agents with MCP tools - Fan-in aggregation to produce single risk assessment - LLM-based risk scoring -- Human-in-the-loop for high-risk cases +- Human-in-the-loop via ctx.request_info() and @response_handler - Checkpointing for workflow pause/resume """ @@ -54,14 +55,12 @@ FileCheckpointStorage, MCPStreamableHTTPTool, RequestInfoEvent, - RequestInfoExecutor, - RequestInfoMessage, - RequestResponse, WorkflowBuilder, WorkflowContext, WorkflowOutputEvent, WorkflowStatusEvent, handler, + response_handler, ) from agent_framework.azure import AzureOpenAIChatClient from pydantic import BaseModel, Field @@ -141,8 +140,8 @@ class FraudRiskAssessment: @dataclass -class AnalystReviewRequest(RequestInfoMessage): - """Request for analyst review sent to RequestInfoExecutor.""" +class AnalystReviewRequest: + """Request for analyst review - used with ctx.request_info().""" assessment: FraudRiskAssessment | None = None prompt: str = "" @@ -678,44 +677,64 @@ async def handle_analysis_results( class ReviewGatewayExecutor(Executor): - """Gateway that routes high-risk assessments to RequestInfoExecutor for human review.""" + """ + Gateway that handles high-risk assessments with human-in-the-loop review. + + Uses the new request_info API: + 1. Receives high-risk assessment + 2. Calls ctx.request_info() to pause workflow and request analyst input + 3. @response_handler receives the analyst's decision + 4. Forwards decision to FraudActionExecutor + """ - def __init__(self, analyst_review_id: str, fraud_action_id: str, id: str = "review_gateway") -> None: + def __init__(self, fraud_action_id: str, id: str = "review_gateway") -> None: super().__init__(id=id) - self._analyst_review_id = analyst_review_id self._fraud_action_id = fraud_action_id + # Store the assessment for use in the response handler + self._pending_assessment: FraudRiskAssessment | None = None @handler async def handle_assessment( - self, assessment: FraudRiskAssessment, ctx: WorkflowContext[AnalystReviewRequest] + self, assessment: FraudRiskAssessment, ctx: WorkflowContext[AnalystDecision] ) -> None: logger.info(f"[ReviewGateway] Routing high-risk assessment {assessment.alert_id} to analyst") + # Store assessment for response handler + self._pending_assessment = assessment + # Create analyst review request request = AnalystReviewRequest( assessment=assessment, prompt=f"Review fraud case for alert {assessment.alert_id}. Risk score: {assessment.overall_risk_score:.2f}. Recommended action: {assessment.recommended_action}", ) - # Send to RequestInfoExecutor - await ctx.send_message(request, target_id=self._analyst_review_id) + # Request info from external analyst - workflow will pause here + # The response will be handled by the @response_handler below + await ctx.request_info(request, AnalystDecision) + logger.info(f"[ReviewGateway] Waiting for analyst decision on {assessment.alert_id}") - @handler + @response_handler async def handle_analyst_response( - self, response: RequestResponse[AnalystReviewRequest, AnalystDecision], ctx: WorkflowContext[AnalystDecision] + self, + original_request: AnalystReviewRequest, + response: AnalystDecision, + ctx: WorkflowContext[AnalystDecision], ) -> None: - logger.info(f"[ReviewGateway] Received analyst decision") - - assessment = response.original_request.assessment if response.original_request else None - decision = response.data + """Handle the analyst's decision and forward to fraud action executor.""" + logger.info(f"[ReviewGateway] Received analyst decision: {response.approved_action}") - if assessment and getattr(decision, "customer_id", None) in (None, 0): - # Now using dataclasses, use replace() to update fields + # Ensure decision has customer_id from original assessment + if original_request.assessment and getattr(response, "customer_id", None) in (None, 0): from dataclasses import replace - decision = replace(decision, customer_id=assessment.customer_id) + response = replace( + response, + customer_id=original_request.assessment.customer_id, + alert_id=original_request.assessment.alert_id, + ) # Forward the analyst decision to fraud action executor - await ctx.send_message(decision, target_id=self._fraud_action_id) + await ctx.send_message(response, target_id=self._fraud_action_id) + logger.info(f"[ReviewGateway] Forwarded decision to fraud action executor") class AutoClearExecutor(Executor): @@ -822,7 +841,7 @@ async def create_fraud_detection_workflow( """ Build the fraud detection workflow. - Topology: + Topology (updated for new request_info API): AlertRouter → [UsagePattern, Location, Billing] → FraudRiskAggregator ↓ (Switch based on risk score) @@ -830,10 +849,14 @@ async def create_fraud_detection_workflow( ┌───────────────────────┴──────────────────────┐ ↓ ↓ (High Risk) (Low Risk) - RequestInfoExecutor AutoClearExecutor - ↓ ↓ - (Analyst Decision) ↓ + ReviewGateway AutoClearExecutor + (uses ctx.request_info ↓ + for human-in-the-loop) ↓ + ↓ ↓ FraudActionExecutor ────────────────────→ FinalNotificationExecutor + + Note: ReviewGateway now uses ctx.request_info() and @response_handler + instead of the deprecated RequestInfoExecutor. """ # Create executors @@ -846,10 +869,8 @@ async def create_fraud_detection_workflow( fraud_action = FraudActionExecutor() final_notification = FinalNotificationExecutor() - # Create human-in-the-loop executors - analyst_review = RequestInfoExecutor(id="analyst_review") + # Create human-in-the-loop executor (now uses ctx.request_info internally) review_gateway = ReviewGatewayExecutor( - analyst_review_id=analyst_review.id, fraud_action_id=fraud_action.id, ) @@ -862,23 +883,21 @@ async def create_fraud_detection_workflow( # Fan-in edge: 3 analysts → Aggregator (waits for all 3) builder.add_fan_in_edges([usage_executor, location_executor, billing_executor], aggregator) - # # Switch/case edges: Aggregator → High risk OR Low risk + # Switch/case edges: Aggregator → High risk OR Low risk builder.add_switch_case_edge_group( aggregator, [ - # High risk → Review Gateway → Analyst review + # High risk → Review Gateway (will request human input via ctx.request_info) Case(condition=lambda assessment: assessment.overall_risk_score >= 0.6, target=review_gateway), # Low risk → Auto clear Default(target=auto_clear), ], ) - # # Review gateway routes to analyst review and back, then to fraud action - builder.add_edge(review_gateway, analyst_review) - builder.add_edge(analyst_review, review_gateway) + # Review gateway → Fraud action (after analyst decision via @response_handler) builder.add_edge(review_gateway, fraud_action) - # # Both paths → Final notification + # Both paths → Final notification builder.add_edge(auto_clear, final_notification) builder.add_edge(fraud_action, final_notification) diff --git a/agentic_ai/workflow/fraud_detection/pyproject.toml b/agentic_ai/workflow/fraud_detection/pyproject.toml index 5c297b7c7..4dcefddb8 100644 --- a/agentic_ai/workflow/fraud_detection/pyproject.toml +++ b/agentic_ai/workflow/fraud_detection/pyproject.toml @@ -6,7 +6,7 @@ readme = "README.md" requires-python = ">=3.12" dependencies = [ "fastapi==0.115.12", - "agent-framework==1.0.0b251007", + "agent-framework==1.0.0b260130", "fastmcp==2.7.1", "flasgger==0.9.7.1", "flask==3.0.3", @@ -15,7 +15,6 @@ dependencies = [ "openai>=1.90.0,<1.110.0", "pydantic==2.11.4", "requests==2.32.4", - "streamlit==1.45.0", "tenacity==8.5.0", "uvicorn>=0.25.0", "websockets>=15.0.1", diff --git a/agentic_ai/workflow/fraud_detection/scenario.md b/agentic_ai/workflow/fraud_detection/scenario.md index c7182eb79..70abc4f6b 100644 --- a/agentic_ai/workflow/fraud_detection/scenario.md +++ b/agentic_ai/workflow/fraud_detection/scenario.md @@ -13,10 +13,10 @@ Contoso’s automated systems flag suspicious account activity, but certain case 3. **Fan-In** to `FraudRiskAggregatorExecutor`: - Produces `FraudRiskScore` and recommended action (lock account, refund charges, ignore). 4. **SwitchCaseEdgeRunner**: - - If risk score ≥ threshold → route to `RequestInfoExecutor` for human fraud analyst review. + - If risk score ≥ threshold → route to `ReviewGatewayExecutor` for human fraud analyst review. - Else → route to `AutoClearExecutor`. -5. **RequestInfoExecutor** → sends “Fraud Case Review Request” to analyst with full context. -6. **Workflow pauses** — checkpoint saved. +5. **ReviewGatewayExecutor** → uses `ctx.request_info()` to request analyst review with full context. +6. **Workflow pauses** — checkpoint saved automatically. 7. **Analyst decides** (approve lock/refund or clear). 8. Workflow resumes: - `FraudActionExecutor` → performs chosen action (e.g., lock account, reverse charges). diff --git a/agentic_ai/workflow/fraud_detection/ui/src/App.jsx b/agentic_ai/workflow/fraud_detection/ui/src/App.jsx index 9cf0a9b66..67f692064 100644 --- a/agentic_ai/workflow/fraud_detection/ui/src/App.jsx +++ b/agentic_ai/workflow/fraud_detection/ui/src/App.jsx @@ -1,11 +1,13 @@ -import { useState, useCallback, useEffect } from 'react'; +import React, { useState, useCallback, useEffect } from 'react'; import { Box, ThemeProvider, + createTheme, CssBaseline, AppBar, Toolbar, Typography, + Container, Paper, Grid, } from '@mui/material'; @@ -15,18 +17,32 @@ import ControlPanel from './components/ControlPanel'; import AnalystDecisionPanel from './components/AnalystDecisionPanel'; import EventLog from './components/EventLog'; import { useWebSocket } from './hooks/useWebSocket'; -import { fetchAlerts, startWorkflow, submitDecision } from './utils/api'; -import { isDuplicateEvent } from './utils/helpers'; -import { EVENT_TYPES } from './constants/workflow'; -import { API_CONFIG, APP_CONFIG } from './constants/config'; -import theme from './theme'; - -/** - * Main application component for the Fraud Detection Workflow Visualizer - * Manages the state and orchestration of workflow visualization, controls, and event logging - */ + +const theme = createTheme({ + palette: { + mode: 'light', + primary: { + main: '#1976d2', + }, + secondary: { + main: '#dc004e', + }, + success: { + main: '#4caf50', + }, + warning: { + main: '#ff9800', + }, + error: { + main: '#f44336', + }, + }, + typography: { + fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif', + }, +}); + function App() { - // State management const [alerts, setAlerts] = useState([]); const [selectedAlert, setSelectedAlert] = useState(null); const [workflowRunning, setWorkflowRunning] = useState(false); @@ -34,57 +50,50 @@ function App() { const [pendingDecision, setPendingDecision] = useState(null); const [executorStates, setExecutorStates] = useState({}); - // WebSocket connection for real-time updates - const { lastMessage, sendMessage } = useWebSocket(API_CONFIG.WS_URL); + // WebSocket hook for real-time updates + const { lastMessage, sendMessage } = useWebSocket('ws://localhost:8001/ws'); - /** - * Load sample alerts on component mount - */ + // Load sample alerts on mount useEffect(() => { - const loadAlerts = async () => { - try { - const alertsData = await fetchAlerts(); - setAlerts(alertsData); - } catch (error) { - console.error('Failed to load alerts:', error); - } - }; - - loadAlerts(); + fetch('/api/alerts') + .then((res) => res.json()) + .then((data) => setAlerts(data.alerts)) + .catch((err) => console.error('Error loading alerts:', err)); }, []); - /** - * Handle incoming WebSocket messages - * Process different event types and update application state accordingly - */ + // Handle WebSocket messages useEffect(() => { if (!lastMessage) return; try { const event = lastMessage; - // Add to event log - prevent duplicates + // Add to event log - prevent duplicates by checking timestamp + type + executor_id setEvents((prev) => { - return isDuplicateEvent(event, prev) ? prev : [...prev, event]; + const eventKey = `${event.timestamp}-${event.type || event.event_type}-${event.executor_id || ''}`; + const isDuplicate = prev.some( + (e) => `${e.timestamp}-${e.type || e.event_type}-${e.executor_id || ''}` === eventKey + ); + return isDuplicate ? prev : [...prev, event]; }); // Handle workflow initialization - if (event.type === EVENT_TYPES.WORKFLOW_INITIALIZING) { + if (event.type === 'workflow_initializing') { // Keep workflow running flag true, just show initialization message } // Handle workflow started - if (event.type === EVENT_TYPES.WORKFLOW_STARTED) { + if (event.type === 'workflow_started') { // Workflow is now running } // Update executor states based on event type - if (event.event_type === EVENT_TYPES.EXECUTOR_INVOKED) { + if (event.event_type === 'executor_invoked') { setExecutorStates((prev) => ({ ...prev, [event.executor_id]: 'running', })); - } else if (event.event_type === EVENT_TYPES.EXECUTOR_COMPLETED) { + } else if (event.event_type === 'executor_completed') { setExecutorStates((prev) => ({ ...prev, [event.executor_id]: 'completed', @@ -92,13 +101,13 @@ function App() { } // Handle decision required - if (event.type === EVENT_TYPES.DECISION_REQUIRED) { + if (event.type === 'decision_required') { setPendingDecision(event); setWorkflowRunning(false); } // Handle workflow completion - if (event.type === EVENT_TYPES.WORKFLOW_COMPLETED || event.type === EVENT_TYPES.WORKFLOW_ERROR) { + if (event.type === 'workflow_completed' || event.type === 'workflow_error') { setWorkflowRunning(false); // Keep all executor states as-is (they should already be 'completed') } @@ -107,10 +116,6 @@ function App() { } }, [lastMessage]); - /** - * Start a workflow for the selected alert - * @param {Object} alert - The alert object to process - */ const handleStartWorkflow = useCallback(async (alert) => { console.log('Starting workflow for alert:', alert); setSelectedAlert(alert); @@ -120,7 +125,15 @@ function App() { setPendingDecision(null); try { - const data = await startWorkflow(alert); + const response = await fetch('/api/workflow/start', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(alert), + }); + + const data = await response.json(); console.log('Workflow started:', data); } catch (error) { console.error('Error starting workflow:', error); @@ -128,15 +141,19 @@ function App() { } }, []); - /** - * Submit analyst decision and resume workflow - * @param {Object} decision - The decision object containing analyst's input - */ const handleSubmitDecision = useCallback(async (decision) => { console.log('Submitting decision:', decision); try { - const data = await submitDecision(decision); + const response = await fetch('/api/workflow/decision', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(decision), + }); + + const data = await response.json(); console.log('Decision submitted:', data); setPendingDecision(null); @@ -149,13 +166,13 @@ function App() { return ( - + {/* App Bar */} - {APP_CONFIG.TITLE} + Fraud Detection Workflow Visualizer Real-time Multi-Agent Workflow Monitoring @@ -164,29 +181,27 @@ function App() { {/* Main Content */} - + {/* Left Column - Controls and Decision Panel */} - - - + + + {pendingDecision && ( + - - {pendingDecision && ( - - )} - + )} {/* Center Column - Workflow Visualization */} - + Workflow Graph @@ -203,14 +218,14 @@ function App() { {/* Right Column - Event Log */} - + - + ); } -export default App; +export default App; \ No newline at end of file diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx index c633fd731..d2cea18f6 100644 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx +++ b/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx @@ -45,13 +45,15 @@ function AnalystDecisionPanel({ decision, onSubmit }) { AI Analysis - + - {decision.data.reasoning} + {decision.data.reasoning.length > 500 + ? decision.data.reasoning.substring(0, 500) + '...' + : decision.data.reasoning} diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx index c7b361301..1852c058f 100644 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx +++ b/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx @@ -51,7 +51,7 @@ const initialNodes = [ id: 'review_gateway', type: 'custom', position: { x: 550, y: 500 }, - data: { label: 'Review Gateway', status: 'idle', description: 'Routes to analyst review' }, + data: { label: 'Review Gateway', status: 'idle', description: 'Human analyst review (pauses workflow)' }, }, { id: 'auto_clear_executor', @@ -59,22 +59,16 @@ const initialNodes = [ position: { x: 250, y: 500 }, data: { label: 'Auto Clear', status: 'idle', description: 'Auto-clears low risk' }, }, - { - id: 'analyst_review', - type: 'custom', - position: { x: 550, y: 650 }, - data: { label: 'Analyst Review', status: 'idle', description: 'Human review required' }, - }, { id: 'fraud_action_executor', type: 'custom', - position: { x: 550, y: 800 }, + position: { x: 550, y: 650 }, data: { label: 'Fraud Action', status: 'idle', description: 'Execute fraud action' }, }, { id: 'final_notification_executor', type: 'custom', - position: { x: 400, y: 950 }, + position: { x: 400, y: 800 }, data: { label: 'Final Notification', status: 'idle', description: 'Send notifications' }, }, ]; @@ -94,10 +88,8 @@ const initialEdges = [ { id: 'e3-1', source: 'fraud_risk_aggregator', target: 'review_gateway', label: 'High Risk', style: { stroke: '#f44336' } }, { id: 'e3-2', source: 'fraud_risk_aggregator', target: 'auto_clear_executor', label: 'Low Risk', style: { stroke: '#4caf50' } }, - // Review loop - { id: 'e4-1', source: 'review_gateway', target: 'analyst_review' }, - { id: 'e4-2', source: 'analyst_review', target: 'review_gateway', animated: true, style: { stroke: '#ff9800' } }, - { id: 'e4-3', source: 'review_gateway', target: 'fraud_action_executor' }, + // Review gateway to fraud action (human review happens via request_info, then proceeds) + { id: 'e4-1', source: 'review_gateway', target: 'fraud_action_executor', animated: true, style: { stroke: '#ff9800' } }, // Final paths { id: 'e5-1', source: 'auto_clear_executor', target: 'final_notification_executor' }, diff --git a/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js b/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js index 531588e32..dd9a4f13d 100644 --- a/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js +++ b/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js @@ -3,6 +3,7 @@ import { WS_CONFIG } from '../constants/config'; /** * Custom hook for managing WebSocket connections with automatic reconnection + * Uses a message queue to ensure no messages are lost when they arrive quickly * @param {string} url - WebSocket URL to connect to * @returns {Object} Object containing lastMessage, readyState, and sendMessage function */ @@ -12,6 +13,29 @@ export function useWebSocket(url) { const ws = useRef(null); const reconnectTimeout = useRef(null); const reconnectAttempts = useRef(0); + + // Message queue to prevent lost messages during rapid updates + const messageQueue = useRef([]); + const processingQueue = useRef(false); + + // Process messages from queue one at a time + const processQueue = useCallback(() => { + if (processingQueue.current || messageQueue.current.length === 0) { + return; + } + + processingQueue.current = true; + const message = messageQueue.current.shift(); + + // Use setTimeout to ensure React processes each state update + setLastMessage(message); + + // Schedule next message processing + setTimeout(() => { + processingQueue.current = false; + processQueue(); + }, 0); + }, []); const connect = useCallback(() => { try { @@ -26,7 +50,9 @@ export function useWebSocket(url) { ws.current.onmessage = (event) => { try { const data = JSON.parse(event.data); - setLastMessage(data); + // Add to queue instead of directly setting state + messageQueue.current.push(data); + processQueue(); } catch (error) { console.error('Error parsing WebSocket message:', error); } @@ -56,7 +82,7 @@ export function useWebSocket(url) { } catch (error) { console.error('Error creating WebSocket:', error); } - }, [url]); + }, [url, processQueue]); useEffect(() => { connect(); diff --git a/agentic_ai/workflow/fraud_detection/uv.lock b/agentic_ai/workflow/fraud_detection/uv.lock index 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a/agentic_ai/workflow/fraud_detection_durable/README.md b/agentic_ai/workflow/fraud_detection_durable/README.md new file mode 100644 index 000000000..5aa5c7753 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/README.md @@ -0,0 +1,360 @@ +# Durable Fraud Detection Workflow + +A hybrid architecture combining **Workflow** (complex topology) and **Durable Task** (durability, HITL) for enterprise-grade fraud detection. + +## 🏗️ Architecture + +``` +┌─────────────────────────────────────────────────────────────────────────┐ +│ DURABLE TASK ORCHESTRATION (Outer Layer) │ +│ Handles: Durability, Long Waits, Crash Recovery │ +│ │ +│ ┌─────────────────────────────────────────────────────────────────┐ │ +│ │ 1. Receive Alert │ │ +│ │ 2. Call "run_fraud_analysis" Activity ──────────────────────┐ │ │ +│ │ │ │ │ +│ │ ┌─────────────────────────────────────────────────────┐ │ │ │ +│ │ │ WORKFLOW (Inner Layer - Activity) │ │ │ │ +│ │ │ Handles: Complex Topology, Fast Execution │ │ │ │ +│ │ │ │ │ │ │ +│ │ │ AlertRouter │ │ │ │ +│ │ │ ↓ │ │ │ │ +│ │ │ ┌───┴───┬───────┐ (fan-out) │ │ │ │ +│ │ │ ↓ ↓ ↓ │ │ │ │ +│ │ │ Usage Location Billing │ │ │ │ +│ │ │ └───────┼───────┘ (fan-in) │ │ │ │ +│ │ │ ↓ │ │ │ │ +│ │ │ Aggregator (LLM) │ │ │ │ +│ │ │ ↓ │ │ │ │ +│ │ │ Returns: FraudRiskAssessment │ │ │ │ +│ │ └─────────────────────────────────────────────────────┘ │ │ │ +│ │ ◄─────┘ │ │ +│ │ 3. Check Risk Score (simple if/else) │ │ +│ │ │ │ +│ │ IF risk >= 0.6: │ │ +│ │ → notify_analyst Activity │ │ +│ │ → wait_for_external_event("AnalystDecision") ⏸️ │ │ +│ │ → execute_fraud_action Activity │ │ +│ │ │ │ +│ │ ELSE: │ │ +│ │ → auto_clear Activity │ │ +│ │ │ │ +│ │ 4. send_notification Activity │ │ +│ └─────────────────────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────────────────────┘ +``` + +## 🎯 Why Hybrid Architecture? + +| Feature | Workflow Only | Durable Task Only | **Hybrid (This)** | +|---------|--------------|-------------------|-------------------| +| Complex topology (fan-out/fan-in) | ✅ Easy | ❌ Manual | ✅ Easy | +| Crash recovery | ❌ Lost state | ✅ Automatic | ✅ Automatic | +| Human-in-the-loop | ⚠️ Manual checkpoints | ✅ Built-in events | ✅ Built-in events | +| Timeout handling | ❌ Not built-in | ✅ Native timers | ✅ Native timers | +| Long waits (hours/days) | ❌ Memory-bound | ✅ Persistent | ✅ Persistent | +| Visibility/Dashboard | ❌ Custom logging | ✅ DTS Dashboard | ✅ DTS Dashboard | + +## 📁 Project Structure + +``` +fraud_detection_durable/ +├── pyproject.toml # Dependencies +├── .env.sample # Environment template +├── fraud_analysis_workflow.py # Inner workflow (fan-out → aggregate) +├── worker.py # DTS Worker with orchestration +├── client.py # CLI client for testing +├── backend.py # FastAPI backend for UI +├── README.md # This file +└── ui/ # React UI + ├── src/ + │ ├── App.jsx # Main app with WebSocket connection + │ └── components/ + │ └── WorkflowVisualizer.jsx # Interactive workflow diagram + └── package.json +``` + +## 🚀 Quick Start + +### Prerequisites + +1. **Docker** - For DTS emulator +2. **Python 3.12+** +3. **Azure OpenAI** - With a deployed model +4. **MCP Server** - Running on port 8000 + +### Step 1: Start Durable Task Scheduler + +```bash +docker run -d --name dts-emulator \ + -p 8080:8080 -p 8082:8082 \ + mcr.microsoft.com/dts/dts-emulator:latest +``` + +Dashboard: http://localhost:8082 + +### Step 2: Start MCP Server + +```bash +cd mcp +uv run mcp_service.py +``` + +### Step 3: Configure Environment + +```bash +cd agentic_ai/workflow/fraud_detection_durable +cp .env.sample .env +# Edit .env with your Azure OpenAI credentials +``` + +### Step 4: Install Dependencies + +```bash +uv sync +``` + +### Step 5: Start Worker + +```bash +uv run worker.py +``` + +### Step 6: Run Tests + +**Option A: CLI Client** +```bash +uv run client.py +``` + +**Option B: FastAPI Backend + React UI** +```bash +# Terminal 1: Backend +uv run backend.py + +# Terminal 2: React UI +cd ui +npm install +npm run dev +``` + +Open http://localhost:5173 to view the interactive workflow UI. + +## 🧪 Test Scenarios + +### 1. High-Risk Alert with Analyst Approval + +``` +Alert: ALERT-001 (multi_country_login, high severity) + ↓ +Workflow: Fan-out to 3 specialists + ↓ +Risk Score: 0.75 (HIGH RISK) + ↓ +⏸️ Waiting for analyst decision... + ↓ +Analyst: "lock_account" + ↓ +✅ Account locked, notification sent +``` + +### 2. Low-Risk Alert with Auto-Clear + +``` +Alert: ALERT-002 (data_spike, low severity) + ↓ +Workflow: Fan-out to 3 specialists + ↓ +Risk Score: 0.35 (LOW RISK) + ↓ +✅ Auto-cleared, notification sent +``` + +### 3. Timeout Escalation + +``` +Alert: ALERT-003 (unusual_charges, high severity) + ↓ +Workflow: Fan-out to 3 specialists + ↓ +Risk Score: 0.80 (CRITICAL) + ↓ +⏸️ Waiting for analyst decision... + ↓ +⏰ Timeout (72 hours) + ↓ +⚠️ Escalated to manager +``` + +## �️ React UI Features + +The interactive React UI provides real-time visualization of the fraud detection workflow: + +### Interactive Workflow Diagram + +- **Real-time Status Updates**: Nodes change color based on execution state + - Gray: Pending + - Blue: Running (with pulse animation) + - Green: Completed + - Red: Failed + +- **Clickable Nodes**: Click any workflow step to see detailed execution info: + - **Tool Calls**: Actual MCP tool calls made (e.g., `get_billing_summary`, `get_data_usage`) + - **Arguments**: Parameters passed to each tool + - **Results**: Output returned from each tool call + - **Step Output**: Final output from the agent step + +### Human-in-the-Loop Panel + +When the workflow reaches the Review Gateway (risk ≥ 0.6): +- Shows analyst decision options: `lock_account`, `flag_review`, `dismiss` +- Displays risk assessment details +- Allows analyst to submit decision via UI + +### Example: Viewing Step Details + +``` +1. Click on "Usage Analyst" node +2. Popover shows: + - Tool Calls (Real): + • get_data_usage(subscription_id=5, start_date="2025-12-01", ...) + → Result: {"total_gb": 45.2, "daily_avg": 1.5, ...} + • get_billing_summary(customer_id=3) + → Result: {"current_balance": 150.00, ...} + - Output: "Usage analysis indicates 300% spike in data..." +``` + +## �📊 DTS Dashboard + +Open http://localhost:8082 to see: + +- All orchestration instances +- Pending external events (analyst decisions) +- Activity execution logs +- Orchestration timeline and status + +![DTS Dashboard](../docs/media/dts-dashboard.png) + +## 🔧 Configuration + +### Environment Variables + +| Variable | Description | Default | +|----------|-------------|---------| +| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint | Required | +| `AZURE_OPENAI_CHAT_DEPLOYMENT` | Deployment name | `gpt-4o` | +| `MCP_SERVER_URI` | MCP server URL | `http://localhost:8000/mcp` | +| `DTS_ENDPOINT` | DTS scheduler URL | `http://localhost:8080` | +| `DTS_TASKHUB` | DTS task hub name | `fraud-detection` | +| `ANALYST_APPROVAL_TIMEOUT_HOURS` | Timeout for analyst review | `72` | + +### Risk Threshold + +Edit `worker.py` to change the risk threshold: + +```python +# Current: 0.6 (60%) +if risk_score >= 0.6: + # High risk path +else: + # Low risk path +``` + +## 🏛️ Key Components + +### 1. Inner Workflow (`fraud_analysis_workflow.py`) + +The workflow handles complex multi-agent topology: + +```python +# Fan-out: Alert → 3 Specialists +builder.add_edge(alert_router, usage_executor) +builder.add_edge(alert_router, location_executor) +builder.add_edge(alert_router, billing_executor) + +# Fan-in: 3 Specialists → Aggregator +builder.add_fan_in_edge( + [usage_executor, location_executor, billing_executor], + aggregator +) +``` + +### 2. DTS Orchestration (`worker.py`) + +The orchestration handles durability and HITL: + +```python +def fraud_detection_orchestration(context, payload): + # Run inner workflow as activity + assessment = yield context.call_activity("run_fraud_analysis", alert) + + if assessment["risk_score"] >= 0.6: + # Wait for analyst with timeout + approval_task = context.wait_for_external_event("AnalystDecision") + timeout_task = context.create_timer(timedelta(hours=72)) + + winner = yield when_any([approval_task, timeout_task]) + + if winner == approval_task: + yield context.call_activity("execute_fraud_action", decision) + else: + yield context.call_activity("escalate_timeout", assessment) + else: + yield context.call_activity("auto_clear_alert", assessment) +``` + +### 3. Activities + +| Activity | Purpose | +|----------|---------| +| `run_fraud_analysis` | Runs inner workflow, returns assessment | +| `notify_analyst` | Sends notification for review | +| `execute_fraud_action` | Executes approved action | +| `auto_clear_alert` | Auto-clears low-risk alerts | +| `escalate_timeout` | Escalates on timeout | +| `send_notification` | Sends final notification | + +## 🔄 Comparison with Original Implementation + +| Aspect | Original (`fraud_detection/`) | Durable (`fraud_detection_durable/`) | +|--------|-------------------------------|-------------------------------------| +| HITL Pattern | `ctx.request_info()` + `@response_handler` | `wait_for_external_event()` | +| Checkpointing | `FileCheckpointStorage` (manual) | DTS (automatic) | +| Timeout | Not built-in | Native `create_timer()` | +| Recovery | Load checkpoint manually | Automatic replay | +| Dashboard | Custom logging | DTS Dashboard | +| Topology | Full workflow | Workflow as activity | + +## 🐛 Troubleshooting + +### "Cannot connect to DTS" + +```bash +# Check if DTS is running +docker ps | grep dts + +# Restart if needed +docker restart dts-emulator +``` + +### "Worker not processing" + +1. Check worker is running: `uv run worker.py` +2. Check logs for errors +3. Verify DTS endpoint in `.env` + +### "Analyst decision not received" + +1. Check instance ID matches +2. Verify event name is `AnalystDecision` +3. Check DTS dashboard for pending events + +## 📚 Related Documentation + +- [Agent Framework Workflow](../human-in-the-loop.md) +- [Durable Task Samples](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/durabletask) +- [Original Fraud Detection](../fraud_detection/README.md) + +## 📜 License + +Copyright (c) Microsoft. All rights reserved. diff --git a/agentic_ai/workflow/fraud_detection_durable/backend.py b/agentic_ai/workflow/fraud_detection_durable/backend.py new file mode 100644 index 000000000..0b7fd926d --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/backend.py @@ -0,0 +1,507 @@ +""" +FastAPI Backend for Durable Fraud Detection. + +This backend provides: +1. REST API to start orchestrations and submit decisions +2. WebSocket for real-time status updates +3. Integration with DTS client + +The UI connects here instead of managing workflow directly. +""" + +import asyncio +import json +import logging +import os +import time +from datetime import datetime +from typing import Any + +from azure.identity import DefaultAzureCredential +from dotenv import load_dotenv +from durabletask.azuremanaged.client import DurableTaskSchedulerClient +from durabletask.client import OrchestrationState +from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel + +# Load environment +load_dotenv() + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# FastAPI app +app = FastAPI( + title="Durable Fraud Detection API", + description="Hybrid Workflow + Durable Task architecture for fraud detection", + version="1.0.0", +) + +# CORS +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:3000", "http://localhost:5173"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Constants +ANALYST_APPROVAL_EVENT = "AnalystDecision" +ORCHESTRATION_NAME = "fraud_detection_orchestration" + + +# ============================================================================ +# Request/Response Models +# ============================================================================ + + +class StartWorkflowRequest(BaseModel): + """Request to start a fraud detection workflow.""" + alert_id: str + customer_id: int + alert_type: str + description: str = "" + severity: str = "medium" + approval_timeout_hours: float = 72.0 + + +class StartWorkflowResponse(BaseModel): + """Response after starting a workflow.""" + instance_id: str + alert_id: str + status: str + + +class AnalystDecisionRequest(BaseModel): + """Analyst decision submitted from UI.""" + instance_id: str + alert_id: str + approved_action: str + analyst_notes: str = "" + analyst_id: str = "analyst_ui" + + +class WorkflowStatusResponse(BaseModel): + """Workflow status response.""" + instance_id: str + status: str + custom_status: str | None + result: dict | None + + +class AlertInfo(BaseModel): + """Sample alert info.""" + alert_id: str + customer_id: int + alert_type: str + description: str + severity: str + + +# ============================================================================ +# Sample Alerts +# ============================================================================ + +SAMPLE_ALERTS = [ + AlertInfo( + alert_id="ALERT-001", + customer_id=1, + alert_type="multi_country_login", + description="Login attempts from USA and Russia within 2 hours", + severity="high", + ), + AlertInfo( + alert_id="ALERT-002", + customer_id=2, + alert_type="data_spike", + description="Data usage increased by 500% in last 24 hours", + severity="medium", + ), + AlertInfo( + alert_id="ALERT-003", + customer_id=3, + alert_type="unusual_charges", + description="Three large purchases totaling $5,000 in 10 minutes", + severity="high", + ), +] + + +# ============================================================================ +# DTS Client +# ============================================================================ + +_dts_client: DurableTaskSchedulerClient | None = None + + +def get_dts_client() -> DurableTaskSchedulerClient: + """Get or create DTS client.""" + global _dts_client + + if _dts_client is None: + taskhub = os.getenv("DTS_TASKHUB", "default") + endpoint = os.getenv("DTS_ENDPOINT", "http://localhost:8080") + + credential = None if endpoint.startswith("http://localhost") else DefaultAzureCredential() + + _dts_client = DurableTaskSchedulerClient( + host_address=endpoint, + secure_channel=not endpoint.startswith("http://localhost"), + taskhub=taskhub, + token_credential=credential, + ) + logger.info(f"DTS client initialized: {endpoint}/{taskhub}") + + return _dts_client + + +# ============================================================================ +# WebSocket Manager +# ============================================================================ + + +class ConnectionManager: + """Manages WebSocket connections for real-time updates.""" + + def __init__(self): + self.active_connections: dict[str, list[WebSocket]] = {} # instance_id -> connections + + async def connect(self, websocket: WebSocket, instance_id: str): + await websocket.accept() + if instance_id not in self.active_connections: + self.active_connections[instance_id] = [] + self.active_connections[instance_id].append(websocket) + logger.info(f"WebSocket connected for instance {instance_id}") + + def disconnect(self, websocket: WebSocket, instance_id: str): + if instance_id in self.active_connections: + self.active_connections[instance_id].remove(websocket) + if not self.active_connections[instance_id]: + del self.active_connections[instance_id] + logger.info(f"WebSocket disconnected for instance {instance_id}") + + async def broadcast(self, instance_id: str, message: dict): + """Broadcast message to all connections watching this instance.""" + if instance_id in self.active_connections: + disconnected = [] + for connection in self.active_connections[instance_id]: + try: + await connection.send_json(message) + except Exception: + disconnected.append(connection) + + for conn in disconnected: + self.disconnect(conn, instance_id) + + +manager = ConnectionManager() + + +# ============================================================================ +# Background Task: Poll DTS for Status Updates +# ============================================================================ + +_polling_tasks: dict[str, asyncio.Task] = {} + + +async def poll_orchestration_status(instance_id: str): + """Background task to poll DTS and broadcast status updates.""" + client = get_dts_client() + last_status = None + last_custom_status = None + + while instance_id in manager.active_connections: + try: + state = client.get_orchestration_state(instance_id) + + if state: + status = state.runtime_status.name + custom_status_raw = state.serialized_custom_status + + # Debug: log raw custom_status + # logger.info(f"[Backend] Raw custom_status: {custom_status_raw[:200] if custom_status_raw else 'None'}") + + # Parse custom_status JSON if present + custom_status = custom_status_raw + step_details = None + status_message = custom_status_raw + + if custom_status_raw: + try: + parsed = json.loads(custom_status_raw) + # Handle double-encoding: if parsed is still a string, parse again + if isinstance(parsed, str): + try: + parsed = json.loads(parsed) + except json.JSONDecodeError: + pass # Keep as string + + if isinstance(parsed, dict): + status_message = parsed.get("message", custom_status_raw) + step_details = parsed.get("step_details") + custom_status = status_message + except json.JSONDecodeError: + pass # Keep original string + + # Only broadcast if status changed + if status != last_status or custom_status_raw != last_custom_status: + message = { + "type": "status_update", + "instance_id": instance_id, + "status": status, + "custom_status": custom_status, + "step_details": step_details, + "timestamp": datetime.now().isoformat(), + } + + # Check if waiting for analyst + if custom_status and "Awaiting analyst" in custom_status: + message["decision_required"] = True + + # Check if completed + if status in ("COMPLETED", "FAILED", "TERMINATED"): + if state.serialized_output: + try: + message["result"] = json.loads(state.serialized_output) + except json.JSONDecodeError: + message["result"] = {"raw": state.serialized_output} + + await manager.broadcast(instance_id, message) + + last_status = status + last_custom_status = custom_status_raw + + # Stop polling if completed + if status in ("COMPLETED", "FAILED", "TERMINATED"): + logger.info(f"Orchestration {instance_id} completed, stopping poll") + break + + except Exception as e: + logger.error(f"Error polling status for {instance_id}: {e}") + + await asyncio.sleep(0.1) # Poll every 100ms for responsive UI + + # Cleanup + if instance_id in _polling_tasks: + del _polling_tasks[instance_id] + + +def start_status_polling(instance_id: str): + """Start background polling for an instance.""" + if instance_id not in _polling_tasks: + task = asyncio.create_task(poll_orchestration_status(instance_id)) + _polling_tasks[instance_id] = task + + +# ============================================================================ +# REST API Endpoints +# ============================================================================ + + +@app.get("/health") +async def health(): + """Health check endpoint.""" + return {"status": "healthy", "timestamp": datetime.now().isoformat()} + + +@app.get("/api/alerts", response_model=list[AlertInfo]) +async def get_alerts(): + """Get sample alerts.""" + return SAMPLE_ALERTS + + +@app.post("/api/workflow/start", response_model=StartWorkflowResponse) +async def start_workflow(request: StartWorkflowRequest): + """Start a new fraud detection orchestration.""" + client = get_dts_client() + + alert = { + "alert_id": request.alert_id, + "customer_id": request.customer_id, + "alert_type": request.alert_type, + "description": request.description, + "timestamp": datetime.now().isoformat(), + "severity": request.severity, + "approval_timeout_hours": request.approval_timeout_hours, + } + + instance_id = client.schedule_new_orchestration( + ORCHESTRATION_NAME, + input=alert, + instance_id=f"fraud-{request.alert_id}-{int(time.time())}", + ) + + logger.info(f"Started orchestration {instance_id} for alert {request.alert_id}") + + return StartWorkflowResponse( + instance_id=instance_id, + alert_id=request.alert_id, + status="started", + ) + + +@app.get("/api/workflow/status/{instance_id}", response_model=WorkflowStatusResponse) +async def get_workflow_status(instance_id: str): + """Get current status of an orchestration.""" + client = get_dts_client() + + state = client.get_orchestration_state(instance_id) + + if not state: + raise HTTPException(status_code=404, detail="Orchestration not found") + + result = None + if state.serialized_output: + try: + result = json.loads(state.serialized_output) + except json.JSONDecodeError: + result = {"raw": state.serialized_output} + + return WorkflowStatusResponse( + instance_id=instance_id, + status=state.runtime_status.name, + custom_status=state.serialized_custom_status, + result=result, + ) + + +@app.post("/api/workflow/decision") +async def submit_decision(request: AnalystDecisionRequest): + """Submit analyst decision for a pending orchestration.""" + client = get_dts_client() + + decision = { + "alert_id": request.alert_id, + "approved_action": request.approved_action, + "analyst_notes": request.analyst_notes, + "analyst_id": request.analyst_id, + } + + client.raise_orchestration_event( + instance_id=request.instance_id, + event_name=ANALYST_APPROVAL_EVENT, + data=decision, + ) + + logger.info(f"Submitted decision for {request.instance_id}: {request.approved_action}") + + # Broadcast decision event + await manager.broadcast(request.instance_id, { + "type": "decision_submitted", + "instance_id": request.instance_id, + "action": request.approved_action, + "timestamp": datetime.now().isoformat(), + }) + + return {"status": "submitted", "instance_id": request.instance_id} + + +# ============================================================================ +# WebSocket Endpoint +# ============================================================================ + + +@app.websocket("/ws/{instance_id}") +async def websocket_endpoint(websocket: WebSocket, instance_id: str): + """ + WebSocket endpoint for real-time status updates. + + Connect to /ws/{instance_id} to receive updates for a specific orchestration. + """ + await manager.connect(websocket, instance_id) + + # Start polling for this instance + start_status_polling(instance_id) + + try: + # Send initial status + client = get_dts_client() + state = client.get_orchestration_state(instance_id) + + if state: + await websocket.send_json({ + "type": "initial_status", + "instance_id": instance_id, + "status": state.runtime_status.name, + "custom_status": state.serialized_custom_status, + }) + + # Keep connection alive + while True: + try: + # Wait for client messages (ping/pong, commands, etc.) + data = await asyncio.wait_for(websocket.receive_text(), timeout=30) + + # Handle client commands + try: + message = json.loads(data) + if message.get("type") == "ping": + await websocket.send_json({"type": "pong"}) + except json.JSONDecodeError: + pass + + except asyncio.TimeoutError: + # Send ping to keep connection alive + try: + await websocket.send_json({"type": "ping"}) + except Exception: + break + + except WebSocketDisconnect: + logger.info(f"WebSocket disconnected for {instance_id}") + except Exception as e: + logger.error(f"WebSocket error for {instance_id}: {e}") + finally: + manager.disconnect(websocket, instance_id) + + +# ============================================================================ +# Startup/Shutdown +# ============================================================================ + + +@app.on_event("startup") +async def startup(): + """Initialize on startup.""" + logger.info("="*60) + logger.info("Starting Durable Fraud Detection Backend") + logger.info("="*60) + + # Pre-initialize DTS client + try: + get_dts_client() + logger.info("✓ DTS client initialized") + except Exception as e: + logger.error(f"Failed to initialize DTS client: {e}") + logger.error("Make sure DTS emulator is running") + + logger.info("Backend ready! 🚀") + + +@app.on_event("shutdown") +async def shutdown(): + """Cleanup on shutdown.""" + logger.info("Shutting down backend...") + + # Cancel polling tasks + for task in _polling_tasks.values(): + task.cancel() + + +# ============================================================================ +# Main +# ============================================================================ + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run( + app, + host="0.0.0.0", + port=8001, + log_level="info", + ) diff --git a/agentic_ai/workflow/fraud_detection_durable/client.py b/agentic_ai/workflow/fraud_detection_durable/client.py new file mode 100644 index 000000000..5f4993949 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/client.py @@ -0,0 +1,398 @@ +""" +Durable Task Client for Fraud Detection Testing. + +This client demonstrates: +1. Starting a fraud detection orchestration +2. Monitoring orchestration status +3. Sending analyst decisions (external events) +4. Viewing results + +Prerequisites: +- Worker must be running (python worker.py) +- DTS emulator running on port 8080 +""" + +import asyncio +import json +import logging +import os +import time +from datetime import datetime + +from azure.identity import DefaultAzureCredential +from dotenv import load_dotenv +from durabletask.azuremanaged.client import DurableTaskSchedulerClient +from durabletask.client import OrchestrationState + +# Load environment +load_dotenv() + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# Constants +ANALYST_APPROVAL_EVENT = "AnalystDecision" +ORCHESTRATION_NAME = "fraud_detection_orchestration" + + +# ============================================================================ +# Sample Alerts +# ============================================================================ + +SAMPLE_ALERTS = { + "ALERT-001": { + "alert_id": "ALERT-001", + "customer_id": 1, + "alert_type": "multi_country_login", + "description": "Login attempts from USA and Russia within 2 hours", + "timestamp": datetime.now().isoformat(), + "severity": "high", + "approval_timeout_hours": 0.05, # 3 minutes for demo + }, + "ALERT-002": { + "alert_id": "ALERT-002", + "customer_id": 2, + "alert_type": "data_spike", + "description": "Data usage increased by 500% in last 24 hours", + "timestamp": datetime.now().isoformat(), + "severity": "medium", + "approval_timeout_hours": 0.05, + }, + "ALERT-003": { + "alert_id": "ALERT-003", + "customer_id": 3, + "alert_type": "unusual_charges", + "description": "Three large purchases totaling $5,000 in 10 minutes", + "timestamp": datetime.now().isoformat(), + "severity": "high", + "approval_timeout_hours": 0.05, + }, +} + + +# ============================================================================ +# Client Helpers +# ============================================================================ + + +def get_client() -> DurableTaskSchedulerClient: + """Create a configured DurableTaskSchedulerClient.""" + taskhub_name = os.getenv("DTS_TASKHUB", "default") + endpoint_url = os.getenv("DTS_ENDPOINT", "http://localhost:8080") + + logger.debug(f"Using DTS endpoint: {endpoint_url}") + logger.debug(f"Using taskhub: {taskhub_name}") + + credential = None if endpoint_url.startswith("http://localhost") else DefaultAzureCredential() + + return DurableTaskSchedulerClient( + host_address=endpoint_url, + secure_channel=not endpoint_url.startswith("http://localhost"), + taskhub=taskhub_name, + token_credential=credential, + ) + + +def start_orchestration(client: DurableTaskSchedulerClient, alert: dict) -> str: + """Start a fraud detection orchestration.""" + instance_id = client.schedule_new_orchestration( + ORCHESTRATION_NAME, + input=alert, + instance_id=f"fraud-{alert['alert_id']}-{int(time.time())}", + ) + logger.info(f"Started orchestration with instance ID: {instance_id}") + return instance_id + + +def get_status(client: DurableTaskSchedulerClient, instance_id: str) -> OrchestrationState | None: + """Get orchestration status.""" + return client.get_orchestration_state(instance_id) + + +def wait_for_status( + client: DurableTaskSchedulerClient, + instance_id: str, + target_status: str = "RUNNING", + timeout: int = 60, + poll_interval: float = 1.0, +) -> OrchestrationState | None: + """Wait for orchestration to reach a specific status.""" + start = time.time() + while time.time() - start < timeout: + state = get_status(client, instance_id) + if state: + current_status = state.runtime_status.name + custom_status = state.custom_status or "" + logger.debug(f"Status: {current_status}, Custom: {custom_status}") + + if current_status == target_status: + return state + + if current_status in ("COMPLETED", "FAILED", "TERMINATED"): + return state + + time.sleep(poll_interval) + + return None + + +def send_analyst_decision( + client: DurableTaskSchedulerClient, + instance_id: str, + alert_id: str, + action: str, + notes: str = "", + analyst_id: str = "analyst_cli", +) -> None: + """Send analyst decision as external event.""" + decision = { + "alert_id": alert_id, + "approved_action": action, + "analyst_notes": notes, + "analyst_id": analyst_id, + } + + logger.info(f"Sending analyst decision: {action}") + client.raise_orchestration_event( + instance_id=instance_id, + event_name=ANALYST_APPROVAL_EVENT, + data=decision, + ) + logger.info("Decision sent successfully") + + +def print_result(state: OrchestrationState | None) -> None: + """Print orchestration result.""" + if not state: + logger.error("No state available") + return + + print(f"\n{'='*60}") + print("ORCHESTRATION RESULT") + print(f"{'='*60}") + print(f"Status: {state.runtime_status.name}") + print(f"Custom Status: {state.serialized_custom_status or 'N/A'}") + + if state.serialized_output: + try: + result = json.loads(state.serialized_output) + print(f"Alert ID: {result.get('alert_id')}") + print(f"Risk Score: {result.get('risk_score', 'N/A')}") + print(f"Action Taken: {result.get('action_taken', 'N/A')}") + print(f"Success: {result.get('success', 'N/A')}") + except json.JSONDecodeError: + print(f"Output: {state.serialized_output}") + + print(f"{'='*60}\n") + + +# ============================================================================ +# Test Scenarios +# ============================================================================ + + +def test_high_risk_with_approval(client: DurableTaskSchedulerClient) -> None: + """ + Test: High-risk alert → Analyst approves → Action executed + """ + print("\n" + "="*70) + print("TEST: High-Risk Alert with Analyst Approval") + print("="*70) + + alert = SAMPLE_ALERTS["ALERT-001"] + print(f"\nAlert: {alert['alert_id']} - {alert['alert_type']}") + print(f"Description: {alert['description']}") + + # Start orchestration + instance_id = start_orchestration(client, alert) + + # Wait for orchestration to request analyst review + print("\n⏳ Waiting for analysis to complete and request analyst review...") + + for _ in range(120): # Wait up to 2 minutes + state = get_status(client, instance_id) + if state: + custom_status = state.serialized_custom_status or "" + if "Awaiting analyst review" in custom_status: + print(f"✓ Orchestration is waiting for analyst: {custom_status}") + break + if state.runtime_status.name in ("COMPLETED", "FAILED"): + print(f"Orchestration ended: {state.runtime_status.name}") + print_result(state) + return + time.sleep(1) + else: + print("⚠️ Timeout waiting for analyst review") + return + + # Simulate analyst decision + print("\n👤 Simulating analyst decision: lock_account") + time.sleep(2) # Simulate analyst thinking + + send_analyst_decision( + client=client, + instance_id=instance_id, + alert_id=alert["alert_id"], + action="lock_account", + notes="Confirmed fraudulent activity - locking account", + analyst_id="analyst_001", + ) + + # Wait for completion + print("\n⏳ Waiting for orchestration to complete...") + state = client.wait_for_orchestration_completion(instance_id, timeout=60) + print_result(state) + + +def test_low_risk_auto_clear(client: DurableTaskSchedulerClient) -> None: + """ + Test: Low-risk alert → Auto-cleared (no human review) + """ + print("\n" + "="*70) + print("TEST: Low-Risk Alert with Auto-Clear") + print("="*70) + + # Modify alert to be low severity + alert = { + **SAMPLE_ALERTS["ALERT-002"], + "severity": "low", + "description": "Minor data usage increase - likely legitimate", + } + print(f"\nAlert: {alert['alert_id']} - {alert['alert_type']}") + print(f"Description: {alert['description']}") + + # Start orchestration + instance_id = start_orchestration(client, alert) + + # Wait for completion (should auto-clear) + print("\n⏳ Waiting for orchestration to complete (should auto-clear)...") + state = client.wait_for_orchestration_completion(instance_id, timeout=120) + print_result(state) + + +def test_timeout_escalation(client: DurableTaskSchedulerClient) -> None: + """ + Test: High-risk alert → Analyst doesn't respond → Timeout escalation + """ + print("\n" + "="*70) + print("TEST: Analyst Timeout Escalation") + print("="*70) + + # Use very short timeout for demo + alert = { + **SAMPLE_ALERTS["ALERT-003"], + "approval_timeout_hours": 0.001, # ~3.6 seconds + } + print(f"\nAlert: {alert['alert_id']} - {alert['alert_type']}") + print(f"Description: {alert['description']}") + print(f"Timeout: {alert['approval_timeout_hours']*3600:.1f} seconds") + + # Start orchestration + instance_id = start_orchestration(client, alert) + + # Wait for completion (will timeout and escalate) + print("\n⏳ Waiting for timeout and escalation...") + state = client.wait_for_orchestration_completion(instance_id, timeout=180) + print_result(state) + + +def interactive_mode(client: DurableTaskSchedulerClient) -> None: + """ + Interactive mode for manual testing. + """ + print("\n" + "="*70) + print("INTERACTIVE MODE") + print("="*70) + + while True: + print("\nOptions:") + print("1. Start new orchestration (ALERT-001)") + print("2. Start new orchestration (ALERT-002)") + print("3. Start new orchestration (ALERT-003)") + print("4. Check status of instance") + print("5. Send analyst decision") + print("6. Exit") + + choice = input("\nChoice: ").strip() + + if choice == "1": + instance_id = start_orchestration(client, SAMPLE_ALERTS["ALERT-001"]) + print(f"Instance ID: {instance_id}") + + elif choice == "2": + instance_id = start_orchestration(client, SAMPLE_ALERTS["ALERT-002"]) + print(f"Instance ID: {instance_id}") + + elif choice == "3": + instance_id = start_orchestration(client, SAMPLE_ALERTS["ALERT-003"]) + print(f"Instance ID: {instance_id}") + + elif choice == "4": + instance_id = input("Instance ID: ").strip() + state = get_status(client, instance_id) + if state: + print(f"Status: {state.runtime_status.name}") + print(f"Custom: {state.custom_status}") + if state.serialized_output: + print(f"Output: {state.serialized_output}") + else: + print("Not found") + + elif choice == "5": + instance_id = input("Instance ID: ").strip() + alert_id = input("Alert ID: ").strip() + action = input("Action (lock_account/refund_charges/clear/both): ").strip() + send_analyst_decision(client, instance_id, alert_id, action) + + elif choice == "6": + break + + +# ============================================================================ +# Main +# ============================================================================ + + +def main(): + """Main entry point.""" + print("\n" + "="*70) + print("Durable Fraud Detection Client") + print("="*70) + print("\nMake sure:") + print("1. DTS emulator is running (docker)") + print("2. Worker is running (python worker.py)") + print("3. MCP server is running") + print("") + + client = get_client() + + print("Select test mode:") + print("1. Run automated tests") + print("2. Interactive mode") + + choice = input("\nChoice: ").strip() + + if choice == "1": + print("\nRunning automated tests...") + + # Test 1: High risk with approval + test_high_risk_with_approval(client) + + # Test 2: Low risk auto-clear + # test_low_risk_auto_clear(client) + + # Test 3: Timeout escalation + # test_timeout_escalation(client) + + print("\n✅ All tests completed!") + + elif choice == "2": + interactive_mode(client) + + else: + print("Invalid choice") + + +if __name__ == "__main__": + main() diff --git a/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py b/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py new file mode 100644 index 000000000..7557707da --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py @@ -0,0 +1,667 @@ +""" +Fraud Analysis Workflow - Inner Workflow for Fan-Out → Aggregate Pattern. + +This workflow handles the complex multi-agent topology: +1. AlertRouter fans out to 3 specialist agents +2. UsagePatternExecutor, LocationAnalysisExecutor, BillingChargeExecutor run in parallel +3. FraudRiskAggregator collects all results and produces FraudRiskAssessment + +This is called as an ACTIVITY from the Durable Task orchestration. +No human-in-the-loop here - that's handled by the outer Durable Task layer. +""" + +import logging +from dataclasses import dataclass, field +from datetime import datetime +from typing import Any + +from pydantic import BaseModel + +from agent_framework import ( + ChatAgent, + Executor, + handler, + WorkflowBuilder, + WorkflowContext, + MCPStreamableHTTPTool, +) +from agent_framework.azure import AzureOpenAIChatClient + +logger = logging.getLogger(__name__) + + +# ============================================================================ +# Helper Functions for Tool Call Extraction +# ============================================================================ + + +def extract_tool_calls(response) -> list[dict]: + """Extract tool calls from an AgentResponse.""" + tool_calls = [] + call_id_to_index = {} # Map call_id to tool_calls index + + for msg in response.messages: + for content in msg.contents: + if content.type == "function_call": + # Parse arguments if they're a string + args = getattr(content, "arguments", {}) + if isinstance(args, str): + try: + import json + args = json.loads(args) + except: + args = {"raw": args} + + call_id = getattr(content, "call_id", None) + idx = len(tool_calls) + tool_calls.append({ + "name": getattr(content, "name", "unknown"), + "arguments": args, + "result": "", + "call_id": call_id, + }) + if call_id: + call_id_to_index[call_id] = idx + + elif content.type == "function_result": + # Match result to existing call by call_id + call_id = getattr(content, "call_id", None) + result = getattr(content, "result", None) + if call_id and call_id in call_id_to_index: + idx = call_id_to_index[call_id] + result_str = str(result)[:500] if result else "" + tool_calls[idx]["result"] = result_str + + elif content.type == "mcp_server_tool_call": + call_id = getattr(content, "call_id", None) + idx = len(tool_calls) + tool_calls.append({ + "name": getattr(content, "tool_name", "unknown"), + "arguments": getattr(content, "arguments", {}), + "result": "", + "call_id": call_id, + }) + if call_id: + call_id_to_index[call_id] = idx + + elif content.type == "mcp_server_tool_result": + call_id = getattr(content, "call_id", None) + output = getattr(content, "output", None) + if call_id and call_id in call_id_to_index: + idx = call_id_to_index[call_id] + result_str = str(output)[:500] if output else "" + tool_calls[idx]["result"] = result_str + + # Remove call_id from final output (not needed for UI) + for tc in tool_calls: + tc.pop("call_id", None) + + return tool_calls + + +# ============================================================================ +# Message Types (Pydantic models for type-safe messaging) +# ============================================================================ + + +class ToolCallInfo(BaseModel): + """Information about a tool call made during analysis.""" + name: str + arguments: dict = {} + result: str = "" + + +class SuspiciousActivityAlert(BaseModel): + """Initial alert from monitoring system.""" + alert_id: str + customer_id: int + alert_type: str + description: str = "" + timestamp: str = "" + severity: str = "medium" + + +class UsageAnalysisResult(BaseModel): + """Result from UsagePatternExecutor.""" + alert_id: str + risk_score: float + findings: str + data_points: list[str] = [] + tool_calls: list[ToolCallInfo] = [] + + +class LocationAnalysisResult(BaseModel): + """Result from LocationAnalysisExecutor.""" + alert_id: str + risk_score: float + findings: str + locations: list[str] = [] + tool_calls: list[ToolCallInfo] = [] + + +class BillingAnalysisResult(BaseModel): + """Result from BillingChargeExecutor.""" + alert_id: str + risk_score: float + findings: str + charges: list[str] = [] + tool_calls: list[ToolCallInfo] = [] + + +class FraudRiskAssessment(BaseModel): + """Final aggregated assessment from FraudRiskAggregator.""" + alert_id: str + customer_id: int + overall_risk_score: float + risk_level: str # "low", "medium", "high", "critical" + recommended_action: str # "clear", "lock_account", "refund_charges", "both" + reasoning: str + usage_findings: str + location_findings: str + billing_findings: str + # Step details for UI + step_details: dict = {} + + +# ============================================================================ +# Executors +# ============================================================================ + + +class AlertRouterExecutor(Executor): + """Routes incoming alerts to all specialist executors (fan-out).""" + + def __init__(self): + super().__init__(id="alert_router") + + @handler + async def handle_alert( + self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[SuspiciousActivityAlert] + ) -> None: + logger.info(f"[AlertRouter] Routing alert {alert.alert_id} to 3 specialist executors") + + # Fan-out: send to all 3 specialist executors + # The workflow edges will handle delivery + await ctx.send_message(alert, target_id="usage_pattern_executor") + await ctx.send_message(alert, target_id="location_analysis_executor") + await ctx.send_message(alert, target_id="billing_charge_executor") + + +class UsagePatternExecutor(Executor): + """Analyzes data usage patterns using MCP tools.""" + + def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient): + super().__init__(id="usage_pattern_executor") + + # Filter MCP tools for usage analysis + allowed_tools = ["get_customer_detail", "get_subscription_detail", "get_data_usage", "search_knowledge_base"] + filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] + + self._agent = ChatAgent( + chat_client=chat_client, + name="UsagePatternAnalyst", + instructions=( + "You are a specialist in analyzing customer data usage patterns. " + "Look for anomalies like sudden spikes, unusual hours, or patterns inconsistent with history. " + "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." + ), + tools=filtered_functions, + ) + + @handler + async def handle_alert( + self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[UsageAnalysisResult] + ) -> None: + logger.info(f"[UsagePatternExecutor] Analyzing alert {alert.alert_id}") + + prompt = f""" +Analyze the usage patterns for customer {alert.customer_id}. +Alert type: {alert.alert_type} +Description: {alert.description} +Severity: {alert.severity} + +Use tools to gather usage data, then assess the risk. + +Respond in this format: +FINDINGS: [Your detailed findings] +RISK_SCORE: [0.0-1.0] +""" + + response = await self._agent.run(prompt) + response_text = response.text if response.text else "" + + # Extract tool calls from response + tool_calls_raw = extract_tool_calls(response) + tool_calls = [ToolCallInfo(name=tc["name"], arguments=tc.get("arguments", {}), result=tc.get("result", "")) for tc in tool_calls_raw] + + # Parse risk score from LLM response (fallback to severity-based) + risk_score = 0.5 + if "RISK_SCORE:" in response_text: + try: + score_line = [line for line in response_text.split("\n") if "RISK_SCORE:" in line][0] + risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) + except (IndexError, ValueError): + # Fallback based on severity + severity_scores = {"low": 0.3, "medium": 0.5, "high": 0.7, "critical": 0.9} + risk_score = severity_scores.get(alert.severity.lower(), 0.5) + else: + severity_scores = {"low": 0.3, "medium": 0.5, "high": 0.7, "critical": 0.9} + risk_score = severity_scores.get(alert.severity.lower(), 0.5) + + result = UsageAnalysisResult( + alert_id=alert.alert_id, + risk_score=risk_score, + findings=response_text or "Analysis completed", + data_points=[], + tool_calls=tool_calls, + ) + + logger.info(f"[UsagePatternExecutor] Completed analysis, risk_score={result.risk_score}, tools={[tc.name for tc in tool_calls]}") + await ctx.send_message(result) + + +class LocationAnalysisExecutor(Executor): + """Analyzes geolocation data for anomalies.""" + + def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient): + super().__init__(id="location_analysis_executor") + + # Filter MCP tools for location analysis + allowed_tools = ["get_customer_detail", "get_security_logs", "search_knowledge_base"] + filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] + + self._agent = ChatAgent( + chat_client=chat_client, + name="LocationAnalysisAgent", + instructions=( + "You are a specialist in analyzing geolocation and security patterns. " + "Look for impossible travel, VPN usage, or login anomalies. " + "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." + ), + tools=filtered_functions, + ) + + @handler + async def handle_alert( + self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[LocationAnalysisResult] + ) -> None: + logger.info(f"[LocationAnalysisExecutor] Analyzing alert {alert.alert_id}") + + prompt = f""" +Analyze the location and security patterns for customer {alert.customer_id}. +Alert type: {alert.alert_type} +Description: {alert.description} +Severity: {alert.severity} + +Use tools to gather security logs and location data, then assess the risk. + +Respond in this format: +FINDINGS: [Your detailed findings] +RISK_SCORE: [0.0-1.0] +""" + + response = await self._agent.run(prompt) + response_text = response.text if response.text else "" + + # Extract tool calls from response + tool_calls_raw = extract_tool_calls(response) + tool_calls = [ToolCallInfo(name=tc["name"], arguments=tc.get("arguments", {}), result=tc.get("result", "")) for tc in tool_calls_raw] + + # Parse risk score from LLM response (fallback to severity-based) + risk_score = 0.5 + if "RISK_SCORE:" in response_text: + try: + score_line = [line for line in response_text.split("\n") if "RISK_SCORE:" in line][0] + risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) + except (IndexError, ValueError): + severity_scores = {"low": 0.3, "medium": 0.5, "high": 0.8, "critical": 0.95} + risk_score = severity_scores.get(alert.severity.lower(), 0.6) + else: + severity_scores = {"low": 0.3, "medium": 0.5, "high": 0.8, "critical": 0.95} + risk_score = severity_scores.get(alert.severity.lower(), 0.6) + + result = LocationAnalysisResult( + alert_id=alert.alert_id, + risk_score=risk_score, + findings=response_text or "Analysis completed", + locations=[], + tool_calls=tool_calls, + ) + + logger.info(f"[LocationAnalysisExecutor] Completed analysis, risk_score={result.risk_score}, tools={[tc.name for tc in tool_calls]}") + await ctx.send_message(result) + + +class BillingChargeExecutor(Executor): + """Analyzes billing and charge patterns.""" + + def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient): + super().__init__(id="billing_charge_executor") + + # Filter MCP tools for billing analysis + allowed_tools = ["get_customer_detail", "get_billing_summary", "get_subscription_detail", + "get_customer_orders", "search_knowledge_base"] + filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] + + self._agent = ChatAgent( + chat_client=chat_client, + name="BillingChargeAnalyst", + instructions=( + "You are a specialist in analyzing billing and charge patterns. " + "Look for unusual purchases, subscription changes, or payment anomalies. " + "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." + ), + tools=filtered_functions, + ) + + @handler + async def handle_alert( + self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[BillingAnalysisResult] + ) -> None: + logger.info(f"[BillingChargeExecutor] Analyzing alert {alert.alert_id}") + + prompt = f""" +Analyze the billing and charge patterns for customer {alert.customer_id}. +Alert type: {alert.alert_type} +Description: {alert.description} +Severity: {alert.severity} + +Use tools to gather billing data and orders, then assess the risk. + +Respond in this format: +FINDINGS: [Your detailed findings] +RISK_SCORE: [0.0-1.0] +""" + + response = await self._agent.run(prompt) + response_text = response.text if response.text else "" + + # Extract tool calls from response + tool_calls_raw = extract_tool_calls(response) + tool_calls = [ToolCallInfo(name=tc["name"], arguments=tc.get("arguments", {}), result=tc.get("result", "")) for tc in tool_calls_raw] + + # Parse risk score from LLM response (fallback to severity-based) + risk_score = 0.4 + if "RISK_SCORE:" in response_text: + try: + score_line = [line for line in response_text.split("\n") if "RISK_SCORE:" in line][0] + risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) + except (IndexError, ValueError): + severity_scores = {"low": 0.2, "medium": 0.4, "high": 0.6, "critical": 0.85} + risk_score = severity_scores.get(alert.severity.lower(), 0.4) + else: + severity_scores = {"low": 0.2, "medium": 0.4, "high": 0.6, "critical": 0.85} + risk_score = severity_scores.get(alert.severity.lower(), 0.4) + + result = BillingAnalysisResult( + alert_id=alert.alert_id, + risk_score=risk_score, + findings=response_text or "Analysis completed", + charges=[], + tool_calls=tool_calls, + ) + + logger.info(f"[BillingChargeExecutor] Completed analysis, risk_score={result.risk_score}, tools={[tc.name for tc in tool_calls]}") + await ctx.send_message(result) + + +# Type alias for fan-in results +AnalysisResult = UsageAnalysisResult | LocationAnalysisResult | BillingAnalysisResult + + +class FraudRiskAggregatorExecutor(Executor): + """Aggregates all analysis results and produces final risk assessment. + + Receives a list of results from fan-in edges (all 3 specialists at once). + """ + + def __init__(self, chat_client: AzureOpenAIChatClient): + super().__init__(id="fraud_risk_aggregator") + self._chat_client = chat_client + + @handler + async def handle_results( + self, results: list[AnalysisResult], ctx: WorkflowContext[None, FraudRiskAssessment] + ) -> None: + """Handle aggregated results from fan-in (receives list of all 3 results).""" + logger.info(f"[Aggregator] Received {len(results)} results for aggregation") + + # Separate results by type + usage: UsageAnalysisResult | None = None + location: LocationAnalysisResult | None = None + billing: BillingAnalysisResult | None = None + + for result in results: + if isinstance(result, UsageAnalysisResult): + usage = result + elif isinstance(result, LocationAnalysisResult): + location = result + elif isinstance(result, BillingAnalysisResult): + billing = result + + if not all([usage, location, billing]): + raise ValueError(f"Expected 3 different result types, got: {[type(r).__name__ for r in results]}") + + alert_id = usage.alert_id + logger.info(f"[Aggregator] Aggregating results for {alert_id}") + + # Use LLM to synthesize findings + agent = ChatAgent( + chat_client=self._chat_client, + name="FraudRiskAggregator", + instructions=( + "You are a senior fraud analyst synthesizing findings from specialist agents. " + "Weigh the evidence, calculate an overall risk score, and recommend an action. " + "Be thorough but decisive. Return a JSON response with the assessment." + ), + ) + + prompt = f""" +Synthesize these fraud analysis findings for alert {alert_id}: + +USAGE ANALYSIS (risk: {usage.risk_score}): +{usage.findings} + +LOCATION ANALYSIS (risk: {location.risk_score}): +{location.findings} + +BILLING ANALYSIS (risk: {billing.risk_score}): +{billing.findings} + +Provide: +1. Overall risk score (0.0-1.0) +2. Risk level (low/medium/high/critical) +3. Recommended action (clear/lock_account/refund_charges/both) +4. Reasoning (1-2 sentences) +""" + + response = await agent.run(prompt) + + # Calculate weighted score + overall_score = (usage.risk_score * 0.3 + location.risk_score * 0.4 + billing.risk_score * 0.3) + + # Determine risk level + if overall_score >= 0.8: + risk_level = "critical" + elif overall_score >= 0.6: + risk_level = "high" + elif overall_score >= 0.4: + risk_level = "medium" + else: + risk_level = "low" + + # Determine recommended action + if overall_score >= 0.6: + recommended_action = "lock_account" + else: + recommended_action = "clear" + + # Helper to safely extract tool calls (handles both ToolCallInfo and dict) + def safe_tool_calls(tool_calls_list): + result = [] + for tc in tool_calls_list: + if hasattr(tc, 'model_dump'): + result.append(tc.model_dump()) + elif isinstance(tc, dict): + result.append(tc) + else: + result.append({"name": str(tc), "arguments": {}, "result": ""}) + return result + + # Build step details for UI + step_details = { + "usage_pattern_executor": { + "status": "completed", + "risk_score": usage.risk_score, + "tool_calls": safe_tool_calls(usage.tool_calls), + "output": usage.findings[:300] if len(usage.findings) > 300 else usage.findings, + }, + "location_analysis_executor": { + "status": "completed", + "risk_score": location.risk_score, + "tool_calls": safe_tool_calls(location.tool_calls), + "output": location.findings[:300] if len(location.findings) > 300 else location.findings, + }, + "billing_charge_executor": { + "status": "completed", + "risk_score": billing.risk_score, + "tool_calls": safe_tool_calls(billing.tool_calls), + "output": billing.findings[:300] if len(billing.findings) > 300 else billing.findings, + }, + "fraud_risk_aggregator": { + "status": "completed", + "risk_score": overall_score, + "tool_calls": [], + "output": f"Aggregated risk: {overall_score:.2f} ({risk_level}). Recommended: {recommended_action}", + }, + } + + assessment = FraudRiskAssessment( + alert_id=alert_id, + customer_id=0, # Would be extracted from original alert + overall_risk_score=overall_score, + risk_level=risk_level, + recommended_action=recommended_action, + reasoning=response.text if response.text else "Based on aggregated analysis", + usage_findings=usage.findings, + location_findings=location.findings, + billing_findings=billing.findings, + step_details=step_details, + ) + + logger.info(f"[Aggregator] Assessment complete: risk={overall_score:.2f}, action={recommended_action}") + await ctx.yield_output(assessment) + + +# ============================================================================ +# Workflow Builder +# ============================================================================ + + +def create_fraud_analysis_workflow( + mcp_tool: MCPStreamableHTTPTool, + chat_client: AzureOpenAIChatClient, +) -> Any: + """ + Create the inner fraud analysis workflow. + + This workflow handles the fan-out → aggregate pattern: + - AlertRouter fans out to 3 specialist agents + - Specialists run in parallel with MCP tools + - Aggregator waits for all 3 and produces FraudRiskAssessment + + Returns: + Workflow: The built workflow ready to run + """ + logger.info("[Workflow] Building fraud analysis workflow...") + + # Create executors + alert_router = AlertRouterExecutor() + usage_executor = UsagePatternExecutor(mcp_tool, chat_client) + location_executor = LocationAnalysisExecutor(mcp_tool, chat_client) + billing_executor = BillingChargeExecutor(mcp_tool, chat_client) + aggregator = FraudRiskAggregatorExecutor(chat_client) + + # Build workflow topology + builder = WorkflowBuilder() + + # Set entry point + builder.set_start_executor(alert_router) + + # Fan-out: Alert Router → 3 Specialists + builder.add_edge(alert_router, usage_executor) + builder.add_edge(alert_router, location_executor) + builder.add_edge(alert_router, billing_executor) + + # Fan-in: 3 Specialists → Aggregator + builder.add_fan_in_edges( + [usage_executor, location_executor, billing_executor], + aggregator + ) + + workflow = builder.build() + logger.info("[Workflow] Fraud analysis workflow built successfully") + + return workflow + + +# ============================================================================ +# Standalone Test +# ============================================================================ + + +async def main(): + """Test the workflow standalone (without Durable Task).""" + import asyncio + import os + from dotenv import load_dotenv + from azure.identity import AzureCliCredential + + load_dotenv() + + # Initialize MCP tool + mcp_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") + mcp_tool = MCPStreamableHTTPTool(name="contoso_mcp", url=mcp_uri, timeout=30) + + async with mcp_tool: + # Initialize chat client + chat_client = AzureOpenAIChatClient( + credential=AzureCliCredential(), + deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o"), + ) + + # Create workflow + workflow = create_fraud_analysis_workflow(mcp_tool, chat_client) + + # Test alert + alert = SuspiciousActivityAlert( + alert_id="TEST-001", + customer_id=1, + alert_type="multi_country_login", + description="Login attempts from USA and Russia within 2 hours", + timestamp=datetime.now().isoformat(), + severity="high", + ) + + print(f"\n{'='*60}") + print(f"Running Fraud Analysis Workflow for Alert: {alert.alert_id}") + print(f"{'='*60}\n") + + # Run workflow + async for event in workflow.run_stream(alert): + print(f"Event: {type(event).__name__}") + if hasattr(event, 'data') and isinstance(event.data, FraudRiskAssessment): + assessment = event.data + print(f"\n{'='*60}") + print("FRAUD RISK ASSESSMENT") + print(f"{'='*60}") + print(f"Alert ID: {assessment.alert_id}") + print(f"Risk Score: {assessment.overall_risk_score:.2f}") + print(f"Risk Level: {assessment.risk_level}") + print(f"Recommended Action: {assessment.recommended_action}") + print(f"Reasoning: {assessment.reasoning}") + + +if __name__ == "__main__": + import asyncio + asyncio.run(main()) diff --git a/agentic_ai/workflow/fraud_detection_durable/pyproject.toml b/agentic_ai/workflow/fraud_detection_durable/pyproject.toml new file mode 100644 index 000000000..6b111a306 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/pyproject.toml @@ -0,0 +1,31 @@ +[project] +name = "fraud-detection-durable" +version = "0.1.0" +description = "Durable Fraud Detection Workflow - Hybrid Workflow + Durable Task Architecture" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + # Agent Framework (includes azure sub-package) + "agent-framework==1.0.0b260130", + + # Durable Task Scheduler + "durabletask-azuremanaged>=1.0.0a1", + + # FastAPI Backend + "fastapi==0.115.12", + "uvicorn>=0.25.0", + "websockets>=15.0.1", + + # Azure & Auth + "azure-identity>=1.15.0", + + # HTTP & Tools + "httpx==0.28.1", + "pydantic==2.11.4", + + # Environment + "python-dotenv>=1.0.0", +] + +[tool.uv] +prerelease = "allow" diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/.dockerignore b/agentic_ai/workflow/fraud_detection_durable/ui/.dockerignore new file mode 100644 index 000000000..778a2ee8b --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/.dockerignore @@ -0,0 +1,30 @@ +# Development dependencies and build artifacts +node_modules/ +dist/ +build/ + +# Logs +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* + +# Environment files +.env +.env.local +.env.*.local + +# IDE +.vscode/ +.idea/ + +# OS +.DS_Store +Thumbs.db + +# Test coverage +coverage/ + +# Temporary files +*.tmp +*.temp diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/.env.example b/agentic_ai/workflow/fraud_detection_durable/ui/.env.example new file mode 100644 index 000000000..02c3939e0 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/.env.example @@ -0,0 +1,6 @@ +# API Configuration +VITE_API_BASE_URL=http://localhost:8001 +VITE_WS_URL=ws://localhost:8001/ws + +# App Configuration +VITE_APP_TITLE=Fraud Detection Workflow Visualizer diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/.eslintrc.cjs b/agentic_ai/workflow/fraud_detection_durable/ui/.eslintrc.cjs new file mode 100644 index 000000000..7617f77ca --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/.eslintrc.cjs @@ -0,0 +1,22 @@ +module.exports = { + root: true, + env: { browser: true, es2020: true }, + extends: [ + 'eslint:recommended', + 'plugin:react/recommended', + 'plugin:react/jsx-runtime', + 'plugin:react-hooks/recommended', + ], + ignorePatterns: ['dist', '.eslintrc.cjs'], + parserOptions: { ecmaVersion: 'latest', sourceType: 'module' }, + settings: { react: { version: '19.0' } }, + plugins: ['react-refresh'], + rules: { + 'react/jsx-no-target-blank': 'off', + 'react-refresh/only-export-components': [ + 'warn', + { allowConstantExport: true }, + ], + 'react/prop-types': 'warn', + }, +} diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/.gitignore b/agentic_ai/workflow/fraud_detection_durable/ui/.gitignore new file mode 100644 index 000000000..aa674a799 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/.gitignore @@ -0,0 +1,31 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? + +# Environment variables +.env +.env.local +.env.production.local +.env.development.local +.env.test.local diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/.prettierignore b/agentic_ai/workflow/fraud_detection_durable/ui/.prettierignore new file mode 100644 index 000000000..8c444c5a1 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/.prettierignore @@ -0,0 +1,7 @@ +node_modules +dist +build +.env +.env.local +*.log +coverage diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/.prettierrc.cjs b/agentic_ai/workflow/fraud_detection_durable/ui/.prettierrc.cjs new file mode 100644 index 000000000..9f6715a36 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/.prettierrc.cjs @@ -0,0 +1,14 @@ +// Prettier Configuration +module.exports = { + semi: true, + trailingComma: 'es5', + singleQuote: true, + printWidth: 100, + tabWidth: 2, + useTabs: false, + arrowParens: 'always', + endOfLine: 'lf', + jsxSingleQuote: false, + bracketSpacing: true, + jsxBracketSameLine: false, +}; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/.vscode/extensions.json b/agentic_ai/workflow/fraud_detection_durable/ui/.vscode/extensions.json new file mode 100644 index 000000000..82a944c17 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/.vscode/extensions.json @@ -0,0 +1,7 @@ +{ + "recommendations": [ + "dbaeumer.vscode-eslint", + "esbenp.prettier-vscode", + "ms-vscode.vscode-typescript-next" + ] +} diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/Dockerfile b/agentic_ai/workflow/fraud_detection_durable/ui/Dockerfile new file mode 100644 index 000000000..f613068f8 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/Dockerfile @@ -0,0 +1,57 @@ +# Build stage +FROM node:22-alpine AS builder + +# Set working directory +WORKDIR /app + +# Add metadata +LABEL maintainer="OpenAI Workshop Team" +LABEL description="Fraud Detection UI - React + Vite Application" + +# Copy package files +COPY package*.json ./ + +# Install dependencies with clean install for reproducible builds +RUN npm ci --only=production=false && \ + npm cache clean --force + +# Copy application source +COPY . . + +# Build the application +RUN npm run build + +# Production stage +FROM node:22-alpine AS production + +# Install dumb-init for proper signal handling +RUN apk add --no-cache dumb-init + +# Set working directory +WORKDIR /app + +# Create a non-root user +RUN addgroup -g 1001 -S nodejs && \ + adduser -S nodejs -u 1001 + +# Install serve globally +RUN npm install -g serve + +# Copy built assets from builder stage +COPY --from=builder --chown=nodejs:nodejs /app/dist ./dist + +# Switch to non-root user +USER nodejs + +# Expose port +EXPOSE 3000 + +# Health check +HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ + CMD wget --no-verbose --tries=1 --spider http://localhost:3000/ || exit 1 + +# Use dumb-init to handle signals properly +ENTRYPOINT ["dumb-init", "--"] + +# Serve the application +CMD ["serve", "-s", "dist", "-l", "3000"] diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/README.md b/agentic_ai/workflow/fraud_detection_durable/ui/README.md new file mode 100644 index 000000000..9a785b969 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/README.md @@ -0,0 +1,247 @@ +# Fraud Detection Workflow Visualizer + +A modern React application for visualizing and managing fraud detection workflows in real-time. Built with Vite, React, Material-UI, and React Flow. + +## Features + +- 🔄 Real-time workflow visualization +- 🎯 Interactive workflow controls +- 📊 Live event logging +- 👤 Human-in-the-loop decision making +- 🔌 WebSocket-based real-time updates +- 🎨 Material-UI based responsive design +- 📈 React Flow powered workflow graphs + +## Tech Stack + +- **Framework**: React 19.2 +- **Build Tool**: Vite 7.2 +- **UI Library**: Material-UI (MUI) 7.3 +- **Graph Visualization**: React Flow 11.11 +- **State Management**: React Hooks +- **Real-time Communication**: WebSocket + +## Prerequisites + +- Node.js 18+ or newer +- npm 9+ or newer + +## Getting Started + +### Installation + +1. Clone the repository +2. Navigate to the project directory: + + ```bash + cd agentic_ai/workflow/fraud_detection/ui + ``` + +3. Install dependencies: + + ```bash + npm install + ``` + +4. Copy the environment file: + + ```bash + cp .env.example .env + ``` + +5. Configure your environment variables in `.env` if needed + +### Development + +Start the development server: + +```bash +npm run dev +``` + +The application will open at `http://localhost:3000` + +### Build + +Build for production: + +```bash +npm run build +``` + +Preview the production build: + +```bash +npm run preview +``` + +### Docker + +Build and run the application in a Docker container: + +```bash +# Build the Docker image +docker build -t fraud-detection-ui:latest . + +# Run the container +docker run -p 3000:3000 fraud-detection-ui:latest + +# Run with custom backend URL +docker run -p 3000:3000 \ + -e VITE_API_BASE_URL=http://your-backend:8001 \ + -e VITE_WS_URL=ws://your-backend:8001/ws \ + fraud-detection-ui:latest +``` + +The Docker image uses a multi-stage build with: + +- **Build stage**: Node.js 22 Alpine with all dependencies +- **Production stage**: Node.js 22 Alpine serving with `serve` +- **Security**: Runs as non-root user (nodejs:1001) +- **Port**: Exposes port 3000 +- **Health check**: Built-in health monitoring + +### Linting + +Run ESLint: + +```bash +npm run lint +``` + +Fix linting issues automatically: + +```bash +npm run lint:fix +``` + +## Project Structure + +```text +ui/ +├── src/ +│ ├── components/ # React components +│ │ ├── AnalystDecisionPanel.jsx +│ │ ├── ControlPanel.jsx +│ │ ├── CustomNode.jsx +│ │ ├── EventLog.jsx +│ │ └── WorkflowVisualizer.jsx +│ ├── hooks/ # Custom React hooks +│ │ └── useWebSocket.js +│ ├── utils/ # Utility functions +│ │ ├── api.js +│ │ ├── helpers.js +│ │ └── uiHelpers.jsx +│ ├── constants/ # Application constants +│ │ ├── config.js +│ │ └── workflow.js +│ ├── theme/ # MUI theme configuration +│ │ └── index.js +│ ├── App.jsx # Main application component +│ ├── main.jsx # Application entry point +│ └── index.css # Global styles +├── public/ # Static assets +├── .env # Environment variables +├── .env.example # Environment variables example +├── .eslintrc.cjs # ESLint configuration +├── .gitignore # Git ignore rules +├── index.html # HTML template +├── jsconfig.json # JavaScript configuration +├── package.json # Project dependencies +├── vite.config.js # Vite configuration +└── README.md # This file +``` + +## Path Aliases + +The project uses path aliases for cleaner imports: + +- `@/` → `src/` +- `@components/` → `src/components/` +- `@hooks/` → `src/hooks/` +- `@utils/` → `src/utils/` +- `@theme/` → `src/theme/` +- `@constants/` → `src/constants/` + +Example: + +```javascript +import WorkflowVisualizer from '@components/WorkflowVisualizer'; +import { useWebSocket } from '@hooks/useWebSocket'; +import theme from '@theme'; +``` + +## Environment Variables + +| Variable | Description | Default | +|----------|-------------|---------| +| `VITE_API_BASE_URL` | Backend API URL | `http://localhost:8001` | +| `VITE_WS_URL` | WebSocket URL | `ws://localhost:8001/ws` | +| `VITE_APP_TITLE` | Application title | `Fraud Detection Workflow Visualizer` | + +## API Integration + +The application connects to a backend API running on `http://localhost:8001` by default. The following endpoints are used: + +- `GET /api/alerts` - Fetch available alerts +- `POST /api/workflow/start` - Start a workflow +- `POST /api/workflow/decision` - Submit analyst decision +- `WS /ws` - WebSocket connection for real-time updates + +## Components + +### Main Components + +- **App.jsx** - Main application component, orchestrates all child components +- **WorkflowVisualizer** - Displays the workflow graph with real-time state updates +- **ControlPanel** - Alert selection and workflow control interface +- **AnalystDecisionPanel** - Human-in-the-loop decision making interface +- **EventLog** - Real-time event logging display +- **CustomNode** - Custom node component for workflow graph + +### Hooks + +- **useWebSocket** - Manages WebSocket connections with automatic reconnection + +## Development Guidelines + +### Code Style + +- Use functional components with hooks +- Follow ESLint rules +- Use path aliases for imports +- Add JSDoc comments for functions +- Keep components small and focused + +### Adding New Components + +1. Create component in `src/components/` +2. Export from the component file +3. Import using path alias: `import Component from '@components/Component'` + +### Adding New Constants + +1. Add constants to appropriate file in `src/constants/` +2. Export named exports +3. Import where needed: `import { CONSTANT } from '@constants/config'` + +## Troubleshooting + +### WebSocket Connection Issues + +- Ensure backend server is running +- Check `VITE_WS_URL` in `.env` +- Check browser console for connection errors + +### Build Issues + +- Clear node_modules and reinstall: `rm -rf node_modules && npm install` +- Clear Vite cache: `rm -rf node_modules/.vite` + +## License + +See the main project LICENSE file. + +## Contributing + +Please read the main project CONTRIBUTING guidelines. diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/index.html b/agentic_ai/workflow/fraud_detection_durable/ui/index.html new file mode 100644 index 000000000..fcdac8966 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/index.html @@ -0,0 +1,16 @@ + + + + + + Fraud Detection Workflow Visualizer + + + +
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"@vitejs/plugin-react": "^5.1.2", + "eslint": "^9.39.1", + "eslint-plugin-react": "^7.37.5", + "eslint-plugin-react-hooks": "^7.0.1", + "eslint-plugin-react-refresh": "^0.4.24", + "vite": "^7.2.7" + } +} diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx new file mode 100644 index 000000000..af279a829 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx @@ -0,0 +1,388 @@ +import React, { useState, useCallback, useEffect, useRef } from 'react'; +import { + Box, + ThemeProvider, + createTheme, + CssBaseline, + AppBar, + Toolbar, + Typography, + Container, + Paper, + Grid, + Chip, +} from '@mui/material'; +import SecurityIcon from '@mui/icons-material/Security'; +import CloudSyncIcon from '@mui/icons-material/CloudSync'; +import WorkflowVisualizer from './components/WorkflowVisualizer'; +import ControlPanel from './components/ControlPanel'; +import AnalystDecisionPanel from './components/AnalystDecisionPanel'; + +const theme = createTheme({ + palette: { + mode: 'light', + primary: { + main: '#1976d2', + }, + secondary: { + main: '#dc004e', + }, + success: { + main: '#4caf50', + }, + warning: { + main: '#ff9800', + }, + error: { + main: '#f44336', + }, + }, + typography: { + fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif', + }, +}); + +function App() { + const [alerts, setAlerts] = useState([]); + const [selectedAlert, setSelectedAlert] = useState(null); + const [workflowRunning, setWorkflowRunning] = useState(false); + const [events, setEvents] = useState([]); + const [pendingDecision, setPendingDecision] = useState(null); + const [executorStates, setExecutorStates] = useState({}); + const [instanceId, setInstanceId] = useState(null); + const [orchestrationStatus, setOrchestrationStatus] = useState(null); + const [stepDetails, setStepDetails] = useState({}); + + const ws = useRef(null); + + // Load sample alerts on mount + useEffect(() => { + fetch('http://localhost:8001/api/alerts') + .then((res) => res.json()) + .then((data) => setAlerts(data)) + .catch((err) => console.error('Error loading alerts:', err)); + }, []); + + const addEvent = useCallback((event) => { + setEvents((prev) => [...prev, { ...event, timestamp: event.timestamp || new Date().toISOString() }]); + }, []); + + // Connect to WebSocket when we have an instance ID + useEffect(() => { + if (!instanceId) return; + + const wsUrl = `ws://localhost:8001/ws/${instanceId}`; + console.log('Connecting to WebSocket:', wsUrl); + + ws.current = new WebSocket(wsUrl); + + ws.current.onopen = () => { + console.log('WebSocket connected for instance:', instanceId); + addEvent({ type: 'websocket_connected', message: `Connected to orchestration ${instanceId}` }); + }; + + ws.current.onmessage = (event) => { + try { + const data = JSON.parse(event.data); + console.log('WebSocket message:', data); + handleWebSocketMessage(data); + } catch (error) { + console.error('Error parsing WebSocket message:', error); + } + }; + + ws.current.onclose = () => { + console.log('WebSocket disconnected'); + }; + + ws.current.onerror = (error) => { + console.error('WebSocket error:', error); + }; + + return () => { + if (ws.current) { + ws.current.close(); + } + }; + }, [instanceId]); + + const handleWebSocketMessage = useCallback((data) => { + // Skip ping/pong messages + if (data.type === 'ping' || data.type === 'pong') return; + + // Add to event log + addEvent(data); + + // Handle different message types + if (data.type === 'status_update' || data.type === 'initial_status') { + setOrchestrationStatus(data.status); + + // Update step details from backend (real tool calls and outputs) + if (data.step_details) { + setStepDetails(data.step_details); + } + + // Map orchestration status to executor states based on custom_status + const customStatus = data.custom_status || ''; + + if (customStatus.includes('Running fraud analysis')) { + // Workflow is running - show specialist agents as active + setExecutorStates({ + alert_router: 'completed', + usage_pattern_executor: 'running', + location_analysis_executor: 'running', + billing_charge_executor: 'running', + }); + } else if (customStatus.includes('Awaiting analyst')) { + // Waiting for analyst - aggregation complete, waiting for human + setExecutorStates({ + alert_router: 'completed', + usage_pattern_executor: 'completed', + location_analysis_executor: 'completed', + billing_charge_executor: 'completed', + fraud_risk_aggregator: 'completed', + review_gateway: 'running', + }); + } else if (customStatus.includes('Executing')) { + // Executing analyst-approved action + setExecutorStates((prev) => ({ + ...prev, + review_gateway: 'completed', + fraud_action_executor: 'running', + })); + } else if (customStatus.includes('Auto-clearing')) { + // Auto-clearing low risk + setExecutorStates({ + alert_router: 'completed', + usage_pattern_executor: 'completed', + location_analysis_executor: 'completed', + billing_charge_executor: 'completed', + fraud_risk_aggregator: 'completed', + auto_clear_executor: 'running', + }); + } else if (customStatus.includes('Sending notification') || customStatus.includes('Sending')) { + // Sending final notification + setExecutorStates((prev) => ({ + ...prev, + fraud_action_executor: prev.fraud_action_executor === 'running' ? 'completed' : prev.fraud_action_executor, + auto_clear_executor: prev.auto_clear_executor === 'running' ? 'completed' : prev.auto_clear_executor, + final_notification_executor: 'running', + })); + } else if (customStatus.includes('Completed') || data.status === 'COMPLETED') { + // Completed - mark everything as done + setExecutorStates((prev) => ({ + ...prev, + fraud_action_executor: prev.fraud_action_executor === 'running' ? 'completed' : prev.fraud_action_executor, + auto_clear_executor: prev.auto_clear_executor === 'running' ? 'completed' : prev.auto_clear_executor, + final_notification_executor: 'completed', + })); + } + + // Check if decision is required + if (data.decision_required) { + setPendingDecision({ + instance_id: instanceId, + alert_id: selectedAlert?.alert_id, + customer_id: selectedAlert?.customer_id, + }); + setWorkflowRunning(false); + } + + // Check if completed + if (data.status === 'COMPLETED' || data.status === 'FAILED') { + setWorkflowRunning(false); + + // Determine which path was taken from the result + const actionTaken = data.result?.action_taken || ''; + const riskScore = data.result?.risk_score || 0; + + // Update step details from result if available + if (data.result?.step_details) { + setStepDetails(data.result.step_details); + } + + // Set the complete final state based on the path taken + if (actionTaken === 'auto_clear' || riskScore < 0.6) { + // Low risk path: auto-clear + setExecutorStates({ + alert_router: 'completed', + usage_pattern_executor: 'completed', + location_analysis_executor: 'completed', + billing_charge_executor: 'completed', + fraud_risk_aggregator: 'completed', + auto_clear_executor: 'completed', + final_notification_executor: 'completed', + }); + } else { + // High risk path: review gateway → fraud action + setExecutorStates({ + alert_router: 'completed', + usage_pattern_executor: 'completed', + location_analysis_executor: 'completed', + billing_charge_executor: 'completed', + fraud_risk_aggregator: 'completed', + review_gateway: 'completed', + fraud_action_executor: 'completed', + final_notification_executor: 'completed', + }); + } + } + + // Handle result + if (data.result) { + addEvent({ type: 'workflow_result', ...data.result }); + } + } + + if (data.type === 'decision_submitted') { + setPendingDecision(null); + setWorkflowRunning(true); + setExecutorStates((prev) => ({ + ...prev, + review_gateway: 'completed', + fraud_action_executor: 'running', + })); + } + }, [instanceId, selectedAlert, addEvent]); + + const handleStartWorkflow = useCallback(async (alert) => { + console.log('Starting workflow for alert:', alert); + setSelectedAlert(alert); + setWorkflowRunning(true); + setEvents([]); + setExecutorStates({ alert_router: 'running' }); + setPendingDecision(null); + setInstanceId(null); + setOrchestrationStatus(null); + setStepDetails({}); // Reset step details for new workflow + + try { + const response = await fetch('http://localhost:8001/api/workflow/start', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + alert_id: alert.alert_id, + customer_id: alert.customer_id, + alert_type: alert.alert_type, + description: alert.description, + severity: alert.severity, + approval_timeout_hours: 0.05, // 3 minutes for demo + }), + }); + + const data = await response.json(); + console.log('Workflow started:', data); + + // Store instance ID - this triggers WebSocket connection + setInstanceId(data.instance_id); + addEvent({ type: 'workflow_started', instance_id: data.instance_id, alert_id: alert.alert_id }); + + } catch (error) { + console.error('Error starting workflow:', error); + setWorkflowRunning(false); + addEvent({ type: 'error', message: error.message }); + } + }, [addEvent]); + + const handleSubmitDecision = useCallback(async (decision) => { + console.log('Submitting decision:', decision); + + try { + const response = await fetch('http://localhost:8001/api/workflow/decision', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + instance_id: instanceId, + alert_id: selectedAlert?.alert_id, + approved_action: decision.approved_action, + analyst_notes: decision.analyst_notes, + analyst_id: 'analyst_ui', + }), + }); + + const data = await response.json(); + console.log('Decision submitted:', data); + addEvent({ type: 'decision_submitted', action: decision.approved_action }); + + } catch (error) { + console.error('Error submitting decision:', error); + addEvent({ type: 'error', message: error.message }); + } + }, [instanceId, selectedAlert, addEvent]); + + return ( + + + + {/* App Bar */} + + + + + Durable Fraud Detection Workflow + + + {instanceId && ( + } + label={`Instance: ${instanceId.substring(0, 20)}...`} + color={orchestrationStatus === 'RUNNING' ? 'primary' : orchestrationStatus === 'COMPLETED' ? 'success' : 'default'} + size="small" + /> + )} + + Hybrid Workflow + Durable Task + + + + + + {/* Main Content */} + + + {/* Left Column - Controls and Decision Panel */} + + + + {pendingDecision && ( + + )} + + + {/* Center Column - Workflow Visualization */} + + + + Workflow Graph + + {selectedAlert + ? `Alert: ${selectedAlert.alert_id} - ${selectedAlert.description}` + : 'Select an alert to start'} + {orchestrationStatus && ` | Status: ${orchestrationStatus}`} + + + + + + + + + + + + ); +} + +export default App; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx new file mode 100644 index 000000000..1c87aea79 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx @@ -0,0 +1,146 @@ +import { useState } from 'react'; +import { + Paper, + Box, + Typography, + Button, + TextField, + Select, + MenuItem, + FormControl, + InputLabel, + Chip, + Alert, + Divider, +} from '@mui/material'; +import GavelIcon from '@mui/icons-material/Gavel'; +import SendIcon from '@mui/icons-material/Send'; +import { ACTION_OPTIONS } from '../constants/workflow'; + +/** + * Panel for analyst to make decisions on fraud alerts (Durable version) + * @param {Object} props - Component props + * @param {Object} props.decision - Decision request object with instance_id, alert_id, customer_id + * @param {Function} props.onSubmit - Callback to submit decision + */ +function AnalystDecisionPanel({ decision, onSubmit }) { + const [selectedAction, setSelectedAction] = useState( + decision.recommended_action || 'block' + ); + const [notes, setNotes] = useState(''); + + const handleSubmit = () => { + onSubmit({ + instance_id: decision.instance_id, + alert_id: decision.alert_id, + customer_id: decision.customer_id, + approved_action: selectedAction, + analyst_notes: notes || 'Analyst decision from UI', + analyst_id: 'analyst_ui', + }); + }; + + return ( + + + + Analyst Review Required + + + + + Human Decision Needed + + + + + + {/* Risk Assessment */} + + + Review Required + + + Alert ID: + + + + Customer: + + + + + {/* Instance Info */} + + + Instance ID + + + {decision.instance_id} + + + + + + {/* Decision Form */} + + Your Decision + + + + setNotes(e.target.value)} + placeholder="Add notes..." + sx={{ '& .MuiInputBase-input': { fontSize: '0.875rem' } }} + /> + + + + ); +} + +export default AnalystDecisionPanel; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/ControlPanel.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/ControlPanel.jsx new file mode 100644 index 000000000..acacc11f1 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/ControlPanel.jsx @@ -0,0 +1,128 @@ +import { useState } from 'react'; +import { + Paper, + Box, + Typography, + Button, + Select, + MenuItem, + FormControl, + InputLabel, + Chip, + CircularProgress, +} from '@mui/material'; +import PlayArrowIcon from '@mui/icons-material/PlayArrow'; +import WarningIcon from '@mui/icons-material/Warning'; +import ErrorIcon from '@mui/icons-material/Error'; +import InfoIcon from '@mui/icons-material/Info'; +import { getSeverityIcon, getSeverityColor } from '../utils/uiHelpers'; + +/** + * Control panel component for selecting alerts and starting workflows + * @param {Object} props - Component props + * @param {Array} props.alerts - Array of alert objects + * @param {Function} props.onStartWorkflow - Callback to start workflow + * @param {boolean} props.workflowRunning - Whether workflow is currently running + * @param {Object} props.selectedAlert - Currently selected alert object + */ +function ControlPanel({ alerts = [], onStartWorkflow, workflowRunning = false, selectedAlert }) { + const [selectedAlertId, setSelectedAlertId] = useState(''); + + const handleStartClick = () => { + const alert = alerts.find((a) => a.alert_id === selectedAlertId); + if (alert) { + onStartWorkflow(alert); + } + }; + + return ( + + + Workflow Control + + + + Select Alert + + + + {selectedAlertId && !workflowRunning && ( + + + Description: + + + {alerts.find((a) => a.alert_id === selectedAlertId)?.description} + + + a.alert_id === selectedAlertId)?.customer_id}`} + size="small" + variant="outlined" + sx={{ height: 18, fontSize: '0.7rem' }} + /> + a.alert_id === selectedAlertId)?.alert_type} + size="small" + variant="outlined" + sx={{ height: 18, fontSize: '0.7rem' }} + /> + + + )} + + + + {selectedAlert && workflowRunning && ( + + + Active Workflow + + + Processing {selectedAlert.alert_id} + + + )} + + ); +} + +export default ControlPanel; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/CustomNode.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/CustomNode.jsx new file mode 100644 index 000000000..52d13030e --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/CustomNode.jsx @@ -0,0 +1,83 @@ +import { Handle, Position } from 'reactflow'; +import { Box, Typography, Paper, Chip } from '@mui/material'; +import CheckCircleIcon from '@mui/icons-material/CheckCircle'; +import HourglassEmptyIcon from '@mui/icons-material/HourglassEmpty'; +import CircleIcon from '@mui/icons-material/Circle'; +import { getNodeStatusColor, getStatusLabel } from '../utils/uiHelpers'; + +/** + * Gets the appropriate icon for node status + * @param {string} status - Node status + * @returns {JSX.Element} Icon component + */ +const getStatusIcon = (status) => { + switch (status) { + case 'running': + return ; + case 'completed': + return ; + default: + return ; + } +}; + +/** + * Custom node component for React Flow workflow visualization + * @param {Object} props - Component props + * @param {Object} props.data - Node data object + */ +function CustomNode({ data = {} }) { + const statusColor = getNodeStatusColor(data.status); + const isActive = data.status === 'running'; + + return ( + + + + + + + {data.label} + + + + + {data.description && ( + + {data.description} + + )} + + + + + ); +} + +export default CustomNode; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/WorkflowVisualizer.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/WorkflowVisualizer.jsx new file mode 100644 index 000000000..e2d399ae3 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/WorkflowVisualizer.jsx @@ -0,0 +1,371 @@ +import { useMemo, useEffect, useState, useCallback } from 'react'; +import ReactFlow, { + Background, + Controls, + MiniMap, + useNodesState, + useEdgesState, +} from 'reactflow'; +import 'reactflow/dist/style.css'; +import { Box, Popover, Paper, Typography, Chip, Divider, List, ListItem, ListItemIcon, ListItemText } from '@mui/material'; +import FunctionsIcon from '@mui/icons-material/Functions'; +import OutputIcon from '@mui/icons-material/Output'; +import AccessTimeIcon from '@mui/icons-material/AccessTime'; +import CheckCircleIcon from '@mui/icons-material/CheckCircle'; +import HourglassEmptyIcon from '@mui/icons-material/HourglassEmpty'; +import CustomNode from './CustomNode'; +import { EXECUTOR_STATES } from '../constants/workflow'; +import { getNodeStatusColor, getStatusLabel } from '../utils/uiHelpers'; + +const nodeTypes = { + custom: CustomNode, +}; + +// Define the workflow graph structure +const initialNodes = [ + { + id: 'alert_router', + type: 'custom', + position: { x: 400, y: 50 }, + data: { label: 'Alert Router', status: 'idle', description: 'Routes alerts to analysts' }, + }, + { + id: 'usage_pattern_executor', + type: 'custom', + position: { x: 150, y: 200 }, + data: { label: 'Usage Analyst', status: 'idle', description: 'Analyzes usage patterns' }, + }, + { + id: 'location_analysis_executor', + type: 'custom', + position: { x: 400, y: 200 }, + data: { label: 'Location Analyst', status: 'idle', description: 'Analyzes location anomalies' }, + }, + { + id: 'billing_charge_executor', + type: 'custom', + position: { x: 650, y: 200 }, + data: { label: 'Billing Analyst', status: 'idle', description: 'Analyzes billing patterns' }, + }, + { + id: 'fraud_risk_aggregator', + type: 'custom', + position: { x: 400, y: 350 }, + data: { label: 'Risk Aggregator', status: 'idle', description: 'Aggregates risk assessment' }, + }, + { + id: 'review_gateway', + type: 'custom', + position: { x: 550, y: 500 }, + data: { label: 'Review Gateway', status: 'idle', description: 'Human analyst review (pauses workflow)' }, + }, + { + id: 'auto_clear_executor', + type: 'custom', + position: { x: 250, y: 500 }, + data: { label: 'Auto Clear', status: 'idle', description: 'Auto-clears low risk' }, + }, + { + id: 'fraud_action_executor', + type: 'custom', + position: { x: 550, y: 650 }, + data: { label: 'Fraud Action', status: 'idle', description: 'Execute fraud action' }, + }, + { + id: 'final_notification_executor', + type: 'custom', + position: { x: 400, y: 800 }, + data: { label: 'Final Notification', status: 'idle', description: 'Send notifications' }, + }, +]; + +const initialEdges = [ + // Fan-out from alert router to analysts + { id: 'e1-1', source: 'alert_router', target: 'usage_pattern_executor', animated: true }, + { id: 'e1-2', source: 'alert_router', target: 'location_analysis_executor', animated: true }, + { id: 'e1-3', source: 'alert_router', target: 'billing_charge_executor', animated: true }, + + // Fan-in to aggregator + { id: 'e2-1', source: 'usage_pattern_executor', target: 'fraud_risk_aggregator' }, + { id: 'e2-2', source: 'location_analysis_executor', target: 'fraud_risk_aggregator' }, + { id: 'e2-3', source: 'billing_charge_executor', target: 'fraud_risk_aggregator' }, + + // Switch case from aggregator + { id: 'e3-1', source: 'fraud_risk_aggregator', target: 'review_gateway', label: 'High Risk', style: { stroke: '#f44336' } }, + { id: 'e3-2', source: 'fraud_risk_aggregator', target: 'auto_clear_executor', label: 'Low Risk', style: { stroke: '#4caf50' } }, + + // Review gateway to fraud action (human review happens via request_info, then proceeds) + { id: 'e4-1', source: 'review_gateway', target: 'fraud_action_executor', animated: true, style: { stroke: '#ff9800' } }, + + // Final paths + { id: 'e5-1', source: 'auto_clear_executor', target: 'final_notification_executor' }, + { id: 'e5-2', source: 'fraud_action_executor', target: 'final_notification_executor' }, +]; + +/** + * Workflow visualizer component using React Flow + * Displays the fraud detection workflow as an interactive graph + * @param {Object} props - Component props + * @param {Object} props.executorStates - Map of executor IDs to their current states + * @param {Object} props.stepDetails - Real step details from backend (tool calls, outputs) + */ +function WorkflowVisualizer({ executorStates = {}, stepDetails = {} }) { + // Popover state + const [anchorEl, setAnchorEl] = useState(null); + const [selectedNode, setSelectedNode] = useState(null); + + // Static descriptions for nodes (fallback) + const nodeDescriptions = useMemo(() => ({ + alert_router: 'Routes incoming fraud alerts to specialist analysts for parallel investigation.', + usage_pattern_executor: 'AI agent that analyzes customer data usage patterns to detect anomalies.', + location_analysis_executor: 'AI agent that detects geographic anomalies and suspicious location patterns.', + billing_charge_executor: 'AI agent that identifies unusual billing patterns and charge irregularities.', + fraud_risk_aggregator: 'Combines analyses from all specialists to calculate overall risk score.', + review_gateway: 'Human-in-the-loop checkpoint. Pauses workflow for analyst approval on high-risk cases.', + auto_clear_executor: 'Automatically clears alerts with low risk scores (< 0.6).', + fraud_action_executor: 'Executes the analyst-approved action (suspend, flag, or additional verification).', + final_notification_executor: 'Sends notification to customer and internal teams about the resolution.', + }), []); + + // Get step info from backend data or fallback + const getStepInfo = useCallback((nodeId) => { + const backendData = stepDetails[nodeId]; + + if (backendData) { + // Real data from backend + return { + toolCalls: backendData.tool_calls || [], + output: backendData.output || '', + riskScore: backendData.risk_score, + hasRealData: true, + }; + } + + // Fallback static data for nodes without backend info + const staticToolCalls = { + alert_router: [{ name: 'distribute_alert', result: 'Sent to 3 analysts' }], + review_gateway: [{ name: 'wait_for_decision', result: 'Analyst decision pending' }], + auto_clear_executor: [{ name: 'clear_alert', result: '' }], + fraud_action_executor: [{ name: 'execute_action', result: '' }], + final_notification_executor: [{ name: 'send_notification', result: '' }], + }; + + return { + toolCalls: staticToolCalls[nodeId] || [], + output: '', + hasRealData: false, + }; + }, [stepDetails]); + + // Handle node click + const handleNodeClick = useCallback((event, node) => { + setAnchorEl(event.target); + setSelectedNode(node); + }, []); + + const handleClosePopover = useCallback(() => { + setAnchorEl(null); + setSelectedNode(null); + }, []); + + // Update nodes with current executor states + const nodes = useMemo(() => { + return initialNodes.map((node) => ({ + ...node, + data: { + ...node.data, + status: executorStates[node.id] || EXECUTOR_STATES.IDLE, + }, + })); + }, [executorStates]); + + const [nodesState, setNodes, onNodesChange] = useNodesState(nodes); + const [edgesState, setEdges, onEdgesChange] = useEdgesState(initialEdges); + + // Update nodes when executor states change + useEffect(() => { + setNodes(nodes); + }, [nodes, setNodes]); + + // Get status icon + const getStatusIcon = (status) => { + if (status === 'running') return ; + if (status === 'completed') return ; + return ; + }; + + return ( + + + + + { + const status = node.data.status; + if (status === 'running') return '#1976d2'; + if (status === 'completed') return '#4caf50'; + return '#9e9e9e'; + }} + /> + + + {/* Node Details Popover */} + + {selectedNode && (() => { + const stepInfo = getStepInfo(selectedNode.id); + return ( + + {/* Header */} + + {getStatusIcon(selectedNode.data.status)} + + {selectedNode.data.label} + + + + {/* Status chip + Risk Score */} + + + {stepInfo.riskScore !== undefined && ( + = 0.6 ? 'error' : 'success'} + variant="outlined" + /> + )} + + + {/* Description */} + + {nodeDescriptions[selectedNode.id] || selectedNode.data.description} + + + + + {/* Function/Tool Calls */} + + + {stepInfo.hasRealData ? 'Tool Calls (Real)' : 'Expected Tools'} + + + {stepInfo.toolCalls.length > 0 ? ( + + {stepInfo.toolCalls.map((tc, idx) => ( + + + + + + + + {tc.result && ( + + + {tc.result.length > 200 ? tc.result.slice(0, 200) + '...' : tc.result} + + + )} + + ))} + + ) : ( + + No tool calls recorded + + )} + + {/* Output Summary (if available) */} + {stepInfo.output && ( + <> + + Analysis Output + + + + {stepInfo.output} + + + + )} + + ); + })()} + + + ); +} + +export default WorkflowVisualizer; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js new file mode 100644 index 000000000..b5bbabc03 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js @@ -0,0 +1,33 @@ +/** + * API configuration constants + */ +export const API_CONFIG = { + BASE_URL: import.meta.env.VITE_API_BASE_URL || 'http://localhost:8001', + WS_URL: import.meta.env.VITE_WS_URL || 'ws://localhost:8001/ws', + ENDPOINTS: { + ALERTS: '/api/alerts', + WORKFLOW_START: '/api/workflow/start', + WORKFLOW_DECISION: '/api/workflow/decision', + }, +}; + +/** + * WebSocket configuration + */ +export const WS_CONFIG = { + RECONNECT_DELAY: 3000, + MAX_RECONNECT_ATTEMPTS: 10, +}; + +/** + * Application constants + */ +export const APP_CONFIG = { + TITLE: import.meta.env.VITE_APP_TITLE || 'Fraud Detection Workflow Visualizer', +}; + +export default { + API_CONFIG, + WS_CONFIG, + APP_CONFIG, +}; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js new file mode 100644 index 000000000..07d791540 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js @@ -0,0 +1,53 @@ +/** + * Workflow event types + */ +export const EVENT_TYPES = { + WORKFLOW_INITIALIZING: 'workflow_initializing', + WORKFLOW_STARTED: 'workflow_started', + WORKFLOW_COMPLETED: 'workflow_completed', + WORKFLOW_ERROR: 'workflow_error', + EXECUTOR_INVOKED: 'executor_invoked', + EXECUTOR_COMPLETED: 'executor_completed', + DECISION_REQUIRED: 'decision_required', + STATUS_CHANGE: 'status_change', + WORKFLOW_OUTPUT: 'workflow_output', +}; + +/** + * Alert severity levels + */ +export const SEVERITY_LEVELS = { + HIGH: 'high', + MEDIUM: 'medium', + LOW: 'low', +}; + +/** + * Executor states + */ +export const EXECUTOR_STATES = { + IDLE: 'idle', + RUNNING: 'running', + COMPLETED: 'completed', + ERROR: 'error', +}; + +/** + * Fraud action options + */ +export const ACTION_OPTIONS = [ + { value: 'clear', label: 'Clear - No Action Needed', color: 'success' }, + { value: 'lock_account', label: 'Lock Account', color: 'error' }, + { value: 'refund_charges', label: 'Refund Charges', color: 'warning' }, + { value: 'both', label: 'Lock Account & Refund', color: 'error' }, +]; + +/** + * WebSocket ready states + */ +export const WS_READY_STATES = { + CONNECTING: 'CONNECTING', + OPEN: 'OPEN', + CLOSING: 'CLOSING', + CLOSED: 'CLOSED', +}; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/index.css b/agentic_ai/workflow/fraud_detection_durable/ui/src/index.css new file mode 100644 index 000000000..0979488ae --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/index.css @@ -0,0 +1,18 @@ +body { + margin: 0; + font-family: 'Roboto', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Oxygen', + 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', + sans-serif; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + background-color: #f5f5f5; +} + +* { + box-sizing: border-box; +} + +#root { + height: 100vh; + width: 100vw; +} diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/main.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/main.jsx new file mode 100644 index 000000000..8e3e8340e --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/main.jsx @@ -0,0 +1,18 @@ +import { StrictMode } from 'react'; +import { createRoot } from 'react-dom/client'; +import App from './App'; +import './index.css'; + +const rootElement = document.getElementById('root'); + +if (!rootElement) { + throw new Error('Failed to find the root element'); +} + +const root = createRoot(rootElement); + +root.render( + + + +); diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/theme/index.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/theme/index.js new file mode 100644 index 000000000..799f49a2f --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/theme/index.js @@ -0,0 +1,84 @@ +import { createTheme } from '@mui/material'; + +/** + * Application theme configuration + * Uses Material-UI theme customization + */ +export const theme = createTheme({ + palette: { + mode: 'light', + primary: { + main: '#1976d2', + light: '#42a5f5', + dark: '#1565c0', + contrastText: '#fff', + }, + secondary: { + main: '#dc004e', + light: '#f33a6a', + dark: '#9a0036', + contrastText: '#fff', + }, + success: { + main: '#4caf50', + light: '#81c784', + dark: '#388e3c', + }, + warning: { + main: '#ff9800', + light: '#ffb74d', + dark: '#f57c00', + }, + error: { + main: '#f44336', + light: '#e57373', + dark: '#d32f2f', + }, + info: { + main: '#2196f3', + light: '#64b5f6', + dark: '#1976d2', + }, + background: { + default: '#f5f5f5', + paper: '#ffffff', + }, + }, + typography: { + fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif', + h6: { + fontWeight: 500, + }, + subtitle1: { + fontWeight: 500, + }, + body1: { + fontSize: '0.875rem', + }, + body2: { + fontSize: '0.75rem', + }, + }, + shape: { + borderRadius: 8, + }, + components: { + MuiButton: { + styleOverrides: { + root: { + textTransform: 'none', + borderRadius: 8, + }, + }, + }, + MuiPaper: { + styleOverrides: { + root: { + borderRadius: 8, + }, + }, + }, + }, +}); + +export default theme; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/api.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/api.js new file mode 100644 index 000000000..26ea9986c --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/api.js @@ -0,0 +1,73 @@ +import { API_CONFIG } from '../constants/config'; + +/** + * Fetches alerts from the API + * @returns {Promise} Array of alerts + */ +export const fetchAlerts = async () => { + try { + const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.ALERTS}`); + if (!response.ok) { + throw new Error(`HTTP error! status: ${response.status}`); + } + const data = await response.json(); + return data.alerts || []; + } catch (error) { + console.error('Error loading alerts:', error); + throw error; + } +}; + +/** + * Starts a workflow for a given alert + * @param {Object} alert - The alert object + * @returns {Promise} Response data + */ +export const startWorkflow = async (alert) => { + try { + const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.WORKFLOW_START}`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(alert), + }); + + if (!response.ok) { + throw new Error(`HTTP error! status: ${response.status}`); + } + + const data = await response.json(); + return data; + } catch (error) { + console.error('Error starting workflow:', error); + throw error; + } +}; + +/** + * Submits an analyst decision + * @param {Object} decision - The decision object + * @returns {Promise} Response data + */ +export const submitDecision = async (decision) => { + try { + const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.WORKFLOW_DECISION}`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(decision), + }); + + if (!response.ok) { + throw new Error(`HTTP error! status: ${response.status}`); + } + + const data = await response.json(); + return data; + } catch (error) { + console.error('Error submitting decision:', error); + throw error; + } +}; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/helpers.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/helpers.js new file mode 100644 index 000000000..8f303b5cc --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/helpers.js @@ -0,0 +1,66 @@ +/** + * Event helpers for workflow event processing + */ + +/** + * Generates a unique key for an event + * @param {Object} event - The event object + * @returns {string} Unique event key + */ +export const generateEventKey = (event) => { + return `${event.timestamp}-${event.type || event.event_type}-${event.executor_id || ''}`; +}; + +/** + * Checks if an event is a duplicate in an array of events + * @param {Object} event - The event to check + * @param {Array} events - Existing events array + * @returns {boolean} True if duplicate + */ +export const isDuplicateEvent = (event, events) => { + const eventKey = generateEventKey(event); + return events.some((e) => generateEventKey(e) === eventKey); +}; + +/** + * Formats a timestamp for display + * @param {string|number} timestamp - The timestamp + * @returns {string} Formatted time string + */ +export const formatTime = (timestamp) => { + const date = new Date(timestamp); + return date.toLocaleTimeString('en-US', { + hour: '2-digit', + minute: '2-digit', + second: '2-digit', + hour12: false, + }); +}; + +/** + * Formats a date and time for display + * @param {string|number} timestamp - The timestamp + * @returns {string} Formatted datetime string + */ +export const formatDateTime = (timestamp) => { + const date = new Date(timestamp); + return date.toLocaleString('en-US', { + month: 'short', + day: 'numeric', + hour: '2-digit', + minute: '2-digit', + second: '2-digit', + hour12: false, + }); +}; + +/** + * Truncates text to a maximum length + * @param {string} text - Text to truncate + * @param {number} maxLength - Maximum length + * @returns {string} Truncated text + */ +export const truncateText = (text, maxLength = 100) => { + if (!text || text.length <= maxLength) return text; + return `${text.substring(0, maxLength)}...`; +}; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/uiHelpers.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/uiHelpers.jsx new file mode 100644 index 000000000..83723375a --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/utils/uiHelpers.jsx @@ -0,0 +1,189 @@ +/** + * UI helper functions for components + */ + +/** + * Gets the appropriate icon for alert severity + * @param {string} severity - Alert severity level + * @param {Object} icons - Icon components object + * @returns {JSX.Element} Icon component + */ +export const getSeverityIcon = (severity, icons) => { + const { ErrorIcon, WarningIcon, InfoIcon } = icons; + switch (severity) { + case 'high': + return ; + case 'medium': + return ; + case 'low': + return ; + default: + return ; + } +}; + +/** + * Gets the appropriate color for alert severity + * @param {string} severity - Alert severity level + * @returns {string} MUI color name + */ +export const getSeverityColor = (severity) => { + switch (severity) { + case 'high': + return 'error'; + case 'medium': + return 'warning'; + case 'low': + return 'info'; + default: + return 'default'; + } +}; + +/** + * Gets the appropriate icon for event type + * @param {Object} event - Event object + * @param {Object} icons - Icon components object + * @returns {JSX.Element} Icon component + */ +export const getEventIcon = (event, icons) => { + const { PlayArrowIcon, CheckCircleIcon, InfoIcon, GavelIcon, ErrorIcon } = icons; + + switch (event.event_type) { + case 'executor_invoked': + return ; + case 'executor_completed': + return ; + case 'status_change': + return ; + case 'workflow_output': + return ; + default: + if (event.type === 'decision_required') { + return ; + } + if (event.type === 'workflow_error') { + return ; + } + return ; + } +}; + +/** + * Gets the appropriate color for event type + * @param {Object} event - Event object + * @returns {string} MUI color name + */ +export const getEventColor = (event) => { + switch (event.event_type) { + case 'executor_invoked': + return 'primary'; + case 'executor_completed': + return 'success'; + case 'status_change': + return 'info'; + case 'workflow_output': + return 'success'; + default: + if (event.type === 'decision_required') { + return 'warning'; + } + if (event.type === 'workflow_error') { + return 'error'; + } + return 'default'; + } +}; + +/** + * Gets the display title for an event + * @param {Object} event - Event object + * @returns {string} Event title + */ +export const getEventTitle = (event) => { + if (event.event_type === 'executor_invoked') { + return `${event.executor_id} started`; + } + if (event.event_type === 'executor_completed') { + return `${event.executor_id} completed`; + } + if (event.event_type === 'status_change') { + return `Status: ${event.status}`; + } + if (event.event_type === 'workflow_output') { + return 'Workflow Output'; + } + if (event.type === 'decision_required') { + return 'Decision Required'; + } + if (event.type === 'workflow_started') { + return 'Workflow Started'; + } + if (event.type === 'workflow_completed') { + return 'Workflow Completed'; + } + if (event.type === 'workflow_error') { + return 'Error Occurred'; + } + return event.type || 'Event'; +}; + +/** + * Gets risk level from risk score + * @param {number} score - Risk score (0-1) + * @returns {string} Risk level + */ +export const getRiskLevel = (score) => { + if (score >= 0.8) return 'Critical'; + if (score >= 0.6) return 'High'; + if (score >= 0.3) return 'Medium'; + return 'Low'; +}; + +/** + * Gets risk color from risk score + * @param {number} score - Risk score (0-1) + * @returns {string} MUI color name + */ +export const getRiskColor = (score) => { + if (score >= 0.8) return 'error'; + if (score >= 0.6) return 'warning'; + if (score >= 0.3) return 'info'; + return 'success'; +}; + +/** + * Gets node status color configuration + * @param {string} status - Node status + * @returns {Object} Color configuration object + */ +export const getNodeStatusColor = (status) => { + switch (status) { + case 'running': + return { bg: '#1976d2', text: '#ffffff' }; + case 'completed': + return { bg: '#4caf50', text: '#ffffff' }; + case 'error': + return { bg: '#f44336', text: '#ffffff' }; + default: + return { bg: '#ffffff', text: '#000000' }; + } +}; + +/** + * Gets status label for display + * @param {string} status - Status value + * @returns {string} Display label + */ +export const getStatusLabel = (status) => { + switch (status) { + case 'running': + return 'Running'; + case 'completed': + return 'Completed'; + case 'error': + return 'Error'; + default: + return 'Idle'; + } +}; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js b/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js new file mode 100644 index 000000000..e05580dca --- /dev/null +++ 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000000000..30b04c06d --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/worker.py @@ -0,0 +1,585 @@ +""" +Durable Task Worker for Fraud Detection. + +This worker hosts: +1. The main fraud_detection_orchestration (outer layer) +2. Activity functions for side effects +3. The inner workflow runs as an activity + +Architecture: +- DTS Orchestration handles: durability, HITL, timeouts, crash recovery +- Inner Workflow handles: complex fan-out/fan-in topology + +Prerequisites: +- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT +- Start Durable Task Scheduler: docker run -d --name dts -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest +- Start MCP Server: cd mcp && uv run mcp_service.py +""" + +import asyncio +import json +import logging +import os +from collections.abc import Generator +from datetime import timedelta +from typing import Any + +from azure.identity import AzureCliCredential, DefaultAzureCredential +from dotenv import load_dotenv +from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker +from durabletask.task import ActivityContext, OrchestrationContext, Task, when_any, when_all +from pydantic import BaseModel, ValidationError + +from agent_framework import MCPStreamableHTTPTool, WorkflowOutputEvent +from agent_framework.azure import AzureOpenAIChatClient + +from fraud_analysis_workflow import ( + SuspiciousActivityAlert, + FraudRiskAssessment, + create_fraud_analysis_workflow, +) + +# Load environment +load_dotenv() + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# Constants +ANALYST_APPROVAL_EVENT = "AnalystDecision" + + +# ============================================================================ +# Input/Output Models +# ============================================================================ + + +class FraudDetectionInput(BaseModel): + """Input for the fraud detection orchestration.""" + alert_id: str + customer_id: int + alert_type: str + description: str = "" + timestamp: str = "" + severity: str = "medium" + approval_timeout_hours: float = 72.0 # 72 hours for analyst review + max_review_attempts: int = 3 + + +class AnalystDecision(BaseModel): + """Human analyst decision.""" + alert_id: str + approved_action: str # "lock_account", "refund_charges", "clear", "both" + analyst_notes: str = "" + analyst_id: str = "analyst" + + +class ActionResult(BaseModel): + """Result from fraud action execution.""" + alert_id: str + action_taken: str + success: bool + details: str + + +# ============================================================================ +# Global Resources (initialized once) +# ============================================================================ + +_mcp_tool: MCPStreamableHTTPTool | None = None +_chat_client: AzureOpenAIChatClient | None = None + + +async def _ensure_resources(): + """Initialize global resources if not already done.""" + global _mcp_tool, _chat_client + + if _mcp_tool is None: + mcp_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") + _mcp_tool = MCPStreamableHTTPTool(name="contoso_mcp", url=mcp_uri, timeout=30) + await _mcp_tool.__aenter__() + logger.info(f"✓ MCP tool initialized at {mcp_uri}") + + if _chat_client is None: + _chat_client = AzureOpenAIChatClient( + credential=AzureCliCredential(), + deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o"), + ) + logger.info("✓ Azure OpenAI client initialized") + + return _mcp_tool, _chat_client + + +# ============================================================================ +# Activity Functions +# ============================================================================ + + +def run_fraud_analysis(context: ActivityContext, alert_dict: dict) -> dict: + """ + Activity that runs the inner fraud analysis workflow. + + This is the bridge between Durable Task and the Workflow: + - Receives alert as dict + - Runs the fan-out → aggregate workflow + - Returns FraudRiskAssessment as dict + """ + logger.info(f"[Activity] run_fraud_analysis starting for alert {alert_dict.get('alert_id')}") + + async def _run_workflow(): + mcp_tool, chat_client = await _ensure_resources() + + # Create the workflow + workflow = create_fraud_analysis_workflow(mcp_tool, chat_client) + + # Create alert object + alert = SuspiciousActivityAlert(**alert_dict) + + # Run workflow and collect output + assessment: FraudRiskAssessment | None = None + + async for event in workflow.run_stream(alert): + if isinstance(event, WorkflowOutputEvent): + if isinstance(event.data, FraudRiskAssessment): + assessment = event.data + break + + if assessment is None: + raise ValueError("Workflow did not produce FraudRiskAssessment") + + # Add customer_id from original alert + assessment_dict = assessment.model_dump() + assessment_dict["customer_id"] = alert.customer_id + + return assessment_dict + + # Run the async workflow synchronously + result = asyncio.run(_run_workflow()) + logger.info(f"[Activity] run_fraud_analysis completed, risk_score={result.get('overall_risk_score')}") + return result + + +def notify_analyst(context: ActivityContext, assessment_dict: dict) -> str: + """ + Activity to notify analyst for review. + + In production, this would: + - Send email/Slack notification + - Create ticket in ITSM system + - Push to analyst dashboard + """ + alert_id = assessment_dict.get("alert_id") + risk_score = assessment_dict.get("overall_risk_score", 0) + recommended_action = assessment_dict.get("recommended_action", "unknown") + + logger.info(f"[Activity] NOTIFICATION: Analyst review required for alert {alert_id}") + logger.info(f"[Activity] Risk Score: {risk_score:.2f}, Recommended: {recommended_action}") + logger.info(f"[Activity] Reasoning: {assessment_dict.get('reasoning', 'N/A')}") + + # In production: send notification via email, Slack, etc. + return f"Analyst notified for alert {alert_id}" + + +def execute_fraud_action(context: ActivityContext, decision_dict: dict) -> dict: + """ + Activity to execute the approved fraud action. + + In production, this would: + - Lock account via API + - Process refunds + - Update fraud database + """ + alert_id = decision_dict.get("alert_id") + action = decision_dict.get("approved_action", "unknown") + analyst_id = decision_dict.get("analyst_id", "unknown") + + logger.info(f"[Activity] Executing fraud action: {action} for alert {alert_id}") + logger.info(f"[Activity] Approved by analyst: {analyst_id}") + + # In production: execute actual action + success = True + details = f"Action '{action}' executed successfully" + + if action == "lock_account": + logger.info(f"[Activity] 🔒 Account locked for alert {alert_id}") + elif action == "refund_charges": + logger.info(f"[Activity] 💰 Charges refunded for alert {alert_id}") + elif action == "both": + logger.info(f"[Activity] 🔒💰 Account locked and charges refunded for alert {alert_id}") + elif action == "clear": + logger.info(f"[Activity] ✅ Alert cleared for {alert_id}") + + return ActionResult( + alert_id=alert_id, + action_taken=action, + success=success, + details=details, + ).model_dump() + + +def auto_clear_alert(context: ActivityContext, assessment_dict: dict) -> dict: + """ + Activity to auto-clear low-risk alerts. + """ + alert_id = assessment_dict.get("alert_id") + risk_score = assessment_dict.get("overall_risk_score", 0) + + logger.info(f"[Activity] Auto-clearing low-risk alert {alert_id} (risk={risk_score:.2f})") + + return ActionResult( + alert_id=alert_id, + action_taken="auto_clear", + success=True, + details=f"Alert auto-cleared due to low risk score ({risk_score:.2f})", + ).model_dump() + + +def escalate_timeout(context: ActivityContext, assessment_dict: dict) -> dict: + """ + Activity to escalate when analyst review times out. + """ + alert_id = assessment_dict.get("alert_id") + + logger.warning(f"[Activity] ⚠️ ESCALATION: Analyst review timed out for alert {alert_id}") + logger.warning(f"[Activity] Escalating to fraud manager...") + + # In production: escalate to manager, auto-lock account, etc. + return ActionResult( + alert_id=alert_id, + action_taken="escalate_timeout", + success=True, + details="Escalated to fraud manager due to review timeout", + ).model_dump() + + +def send_notification(context: ActivityContext, result_dict: dict) -> str: + """ + Activity to send final notification. + """ + alert_id = result_dict.get("alert_id") + action_taken = result_dict.get("action_taken", "unknown") + + logger.info(f"[Activity] Sending final notification for alert {alert_id}") + logger.info(f"[Activity] Action taken: {action_taken}") + + # In production: send customer notification, update audit log + return f"Notification sent for alert {alert_id}, action: {action_taken}" + + +# ============================================================================ +# Main Orchestration +# ============================================================================ + + +def fraud_detection_orchestration( + context: OrchestrationContext, + payload_raw: Any +) -> Generator[Task[Any], Any, dict]: + """ + Main Durable Task orchestration for fraud detection. + + This orchestration: + 1. Runs the inner workflow (fan-out → aggregate) as an activity + 2. Routes based on risk score (simple if/else) + 3. For high risk: waits for analyst decision with timeout + 4. Executes approved action or auto-clears + 5. Sends final notification + + Args: + context: The orchestration context + payload_raw: The input payload (alert data) + + Returns: + dict: Final result with status and action taken + """ + logger.info("[Orchestration] Starting fraud detection orchestration") + + # Validate input + if not isinstance(payload_raw, dict): + raise ValueError("Alert data is required") + + try: + payload = FraudDetectionInput.model_validate(payload_raw) + except ValidationError as exc: + raise ValueError(f"Invalid alert input: {exc}") from exc + + alert_id = payload.alert_id + logger.info(f"[Orchestration] Processing alert {alert_id}") + + context.set_custom_status(json.dumps({ + "message": f"Running fraud analysis for {alert_id}", + "step_details": {}, + "risk_score": None, + })) + + # ======================================================================== + # Step 1: Run the inner workflow (fan-out → aggregate) + # ======================================================================== + + logger.info("[Orchestration] Step 1: Running fraud analysis workflow...") + + alert_dict = { + "alert_id": payload.alert_id, + "customer_id": payload.customer_id, + "alert_type": payload.alert_type, + "description": payload.description, + "timestamp": payload.timestamp, + "severity": payload.severity, + } + + assessment_task: Task[dict] = context.call_activity("run_fraud_analysis", input=alert_dict) + assessment: dict = yield assessment_task + + risk_score = assessment.get("overall_risk_score", 0) + recommended_action = assessment.get("recommended_action", "unknown") + step_details = assessment.get("step_details", {}) + + logger.info(f"[Orchestration] Analysis complete: risk={risk_score:.2f}, recommended={recommended_action}") + + # ======================================================================== + # Step 2: Route based on risk score + # ======================================================================== + + result: dict + + if risk_score >= 0.6: + # HIGH RISK - Human-in-the-loop + logger.info(f"[Orchestration] HIGH RISK ({risk_score:.2f}) - Requiring analyst review") + context.set_custom_status(json.dumps({ + "message": f"Awaiting analyst review (risk={risk_score:.2f})", + "step_details": step_details, + "risk_score": risk_score, + })) + + # Notify analyst + yield context.call_activity("notify_analyst", input=assessment) + + # Wait for analyst decision OR timeout + approval_task: Task[Any] = context.wait_for_external_event(ANALYST_APPROVAL_EVENT) + timeout_task: Task[Any] = context.create_timer( + context.current_utc_datetime + timedelta(hours=payload.approval_timeout_hours) + ) + + logger.info(f"[Orchestration] Waiting for analyst decision (timeout: {payload.approval_timeout_hours}h)") + + winner_task = yield when_any([approval_task, timeout_task]) + + if winner_task == approval_task: + # Analyst responded + decision_data: Any = approval_task.get_result() + logger.info(f"[Orchestration] Received analyst decision: {decision_data}") + + # Parse decision + if isinstance(decision_data, dict): + decision = AnalystDecision.model_validate(decision_data) + else: + # Handle string or other formats + decision = AnalystDecision( + alert_id=alert_id, + approved_action=str(decision_data), + analyst_notes="", + analyst_id="unknown", + ) + + # Update step_details with review_gateway completion + step_details["review_gateway"] = { + "status": "completed", + "tool_calls": [{ + "name": "analyst_decision", + "arguments": {"action": decision.approved_action}, + "result": f"Approved by {decision.analyst_id}: {decision.analyst_notes or 'No notes'}" + }], + "output": f"Action approved: {decision.approved_action}", + } + + context.set_custom_status(json.dumps({ + "message": "Executing analyst-approved action", + "step_details": step_details, + "risk_score": risk_score, + })) + + # Execute the approved action + action_result: dict = yield context.call_activity( + "execute_fraud_action", + input=decision.model_dump() + ) + result = action_result + + # Update step_details with fraud_action_executor completion + step_details["fraud_action_executor"] = { + "status": "completed", + "tool_calls": [{ + "name": "execute_fraud_action", + "arguments": {"action": decision.approved_action, "alert_id": alert_id}, + "result": f"Action executed: {result.get('action_taken', 'unknown')}" + }], + "output": result.get("details", f"Executed action: {decision.approved_action}"), + } + + else: + # Timeout - escalate + logger.warning(f"[Orchestration] Analyst review timed out after {payload.approval_timeout_hours}h") + context.set_custom_status(json.dumps({ + "message": "Review timed out - escalating", + "step_details": step_details, + "risk_score": risk_score, + })) + + escalation_result: dict = yield context.call_activity( + "escalate_timeout", + input=assessment + ) + result = escalation_result + + else: + # LOW RISK - Auto-clear + logger.info(f"[Orchestration] LOW RISK ({risk_score:.2f}) - Auto-clearing") + context.set_custom_status(json.dumps({ + "message": f"Auto-clearing alert (risk={risk_score:.2f})", + "step_details": step_details, + "risk_score": risk_score, + })) + + clear_result: dict = yield context.call_activity("auto_clear_alert", input=assessment) + result = clear_result + + # Update step_details with auto_clear_executor completion + step_details["auto_clear_executor"] = { + "status": "completed", + "tool_calls": [{ + "name": "auto_clear_alert", + "arguments": {"alert_id": alert_id, "risk_score": risk_score}, + "result": f"Alert auto-cleared (low risk: {risk_score:.2f})" + }], + "output": result.get("details", "Alert automatically cleared due to low risk score"), + } + + # ======================================================================== + # Step 3: Send final notification + # ======================================================================== + + logger.info("[Orchestration] Step 3: Sending final notification") + context.set_custom_status(json.dumps({ + "message": "Sending notification", + "step_details": step_details, + "risk_score": risk_score, + })) + + yield context.call_activity("send_notification", input=result) + + # Update step_details with final_notification_executor completion + step_details["final_notification_executor"] = { + "status": "completed", + "tool_calls": [{ + "name": "send_notification", + "arguments": {"alert_id": alert_id, "action_taken": result.get("action_taken")}, + "result": "Notification sent to customer and internal teams" + }], + "output": f"Notification sent for alert {alert_id}", + } + + # ======================================================================== + # Complete + # ======================================================================== + + logger.info(f"[Orchestration] ✅ Fraud detection completed for alert {alert_id}") + context.set_custom_status(json.dumps({ + "message": "Completed", + "step_details": step_details, + "risk_score": risk_score, + })) + + return { + "alert_id": alert_id, + "status": "completed", + "risk_score": risk_score, + "action_taken": result.get("action_taken"), + "success": result.get("success"), + "step_details": step_details, + } + + +# ============================================================================ +# Worker Setup +# ============================================================================ + + +def get_worker( + taskhub: str | None = None, + endpoint: str | None = None, +) -> DurableTaskSchedulerWorker: + """Create a configured DurableTaskSchedulerWorker.""" + taskhub_name = taskhub or os.getenv("DTS_TASKHUB", "default") + endpoint_url = endpoint or os.getenv("DTS_ENDPOINT", "http://localhost:8080") + + logger.info(f"Using DTS endpoint: {endpoint_url}") + logger.info(f"Using taskhub: {taskhub_name}") + + # Use credentials for Azure-hosted DTS, None for local emulator + credential = None if endpoint_url.startswith("http://localhost") else DefaultAzureCredential() + + return DurableTaskSchedulerWorker( + host_address=endpoint_url, + secure_channel=not endpoint_url.startswith("http://localhost"), + taskhub=taskhub_name, + token_credential=credential, + ) + + +def setup_worker(worker: DurableTaskSchedulerWorker) -> None: + """Set up the worker with orchestrations and activities.""" + + logger.info("Registering activities...") + worker.add_activity(run_fraud_analysis) + worker.add_activity(notify_analyst) + worker.add_activity(execute_fraud_action) + worker.add_activity(auto_clear_alert) + worker.add_activity(escalate_timeout) + worker.add_activity(send_notification) + logger.info("✓ Activities registered") + + logger.info("Registering orchestration...") + worker.add_orchestrator(fraud_detection_orchestration) + logger.info("✓ Orchestration registered") + + +async def main(): + """Main entry point for the worker process.""" + logger.info("="*60) + logger.info("Starting Durable Fraud Detection Worker") + logger.info("="*60) + + # Pre-initialize resources + logger.info("Initializing resources...") + try: + await _ensure_resources() + except Exception as e: + logger.error(f"Failed to initialize resources: {e}") + logger.error("Make sure MCP server is running and Azure OpenAI is configured") + return + + # Create and setup worker + worker = get_worker() + setup_worker(worker) + + logger.info("") + logger.info("Worker is ready and listening for orchestrations!") + logger.info("Dashboard: http://localhost:8082") + logger.info("Press Ctrl+C to stop.") + logger.info("") + + try: + worker.start() + + # Keep running + while True: + await asyncio.sleep(1) + except KeyboardInterrupt: + logger.info("Worker shutdown initiated") + + logger.info("Worker stopped") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/workflow/human-in-the-loop.md b/agentic_ai/workflow/human-in-the-loop.md deleted file mode 100644 index ef0c09cfc..000000000 --- a/agentic_ai/workflow/human-in-the-loop.md +++ /dev/null @@ -1,204 +0,0 @@ -# Human-in-the-Loop Patterns in Microsoft Agent Framework - -Human-in-the-loop (HITL) workflows let an AI-driven system pause, route a decision to a human, and resume once guidance arrives. Microsoft Agent Framework provides first-class building blocks—`RequestInfoExecutor`, workflow checkpointing, and the agent wrapper APIs—to make these patterns reliable at production scale. - -This guide explains how the pieces fit together, compares common designs, and walks through end-to-end implementations using the samples in this repository. - -## Why Human-in-the-Loop? - -Use HITL when you need: - -- **Regulated approvals** (finance, healthcare, legal) where humans must sign off. -- **Quality control** for generated content, data extraction, or recommendations. -- **Escalation paths** for ambiguous outcomes where a human provides context the model lacks. -- **Guardrails** that keep LLMs aligned with brand voice, tone, or safety policies. - -## Core Concepts - -| Concept | Description | Key APIs | -| --- | --- | --- | -| `RequestInfoExecutor` | Special executor that surfaces requests to the outside world through function calls, then resumes execution when the response arrives. | `RequestInfoExecutor`, `RequestInfoMessage`, `RequestResponse` | -| Function call bridge | The workflow emits a function call (`WorkflowAgent.REQUEST_INFO_FUNCTION_NAME`) which the host application handles to collect human input. | `FunctionCallContent`, `FunctionResultContent` | -| Workflow checkpointing | Persist runtime state so workflows can pause indefinitely and resume after minutes, hours, or days. | `WorkflowBuilder.with_checkpointing`, `FileCheckpointStorage`, `RedisCheckpointStorage`, `CosmosDBCheckpointStorage` | -| Workflow agent wrapper | Exposes the workflow via the agent protocol so you can drive it with `agent.run()`/`run_stream()` and process function calls uniformly. | `workflow.as_agent()` | - -## High-Level Architecture - -``` -┌──────────────┐ Function Call ┌────────────────────┐ -│ Workflow │ ───────────────────▶ │ External Host/UI │ -│ (RequestInfo │ │ (Chat UI, API, etc) │ -│ Executor) │ ◀─────────────────── │ │ -└────┬─────────┘ Function Result └────────┬───────────┘ - │ │ - │ (Optional) │ - ▼ ▼ -┌──────────────┐ ┌──────────────┐ -│ Checkpoint │◀─────────────────────▶│ Persisted │ -│ Storage │ save/resume state │ Workflow │ -└──────────────┘ └──────────────┘ -``` - -1. An executor sends a `RequestInfoMessage` to `RequestInfoExecutor`. -2. The workflow emits a function call with the request payload and pauses. -3. Your application surfaces the request (e.g., UI prompt, email) and collects the human decision. -4. The decision returns as a function result, which `RequestInfoExecutor` converts into a `RequestResponse`. -5. The workflow resumes where it left off. -6. If checkpointing is enabled, the workflow state is saved before and after the pause. - -## Pattern 1: Synchronous Escalation - -Best for prototypes or workflows where humans respond immediately. - -**Sample:** `python/samples/getting_started/workflows/agents/workflow_as_agent_human_in_the_loop_azure.py` - -### Key Steps - -1. **Define the request message** - - ```python - @dataclass - class HumanReviewRequest(RequestInfoMessage): - agent_request: ReviewRequest | None = None - ``` - -2. **Route to `RequestInfoExecutor`** - - ```python - await ctx.send_message( - HumanReviewRequest(agent_request=request), - target_id=request_info_executor.id, - ) - ``` - -3. **Detect the function call** in the agent response and obtain the arguments via `WorkflowAgent.RequestInfoFunctionArgs`. - -4. **Return the human response** as a `FunctionResultContent`, then call `agent.run(...)` again with a tool-role message. - -### Pros & Cons - -| Pros | Cons | -| --- | --- | -| Simple to reason about | Caller must stay connected while waiting | -| Minimal infrastructure | Not suitable for long delays | - -## Pattern 2: Checkpointed Pause/Resume - -Use this when responses may take minutes or days, or when you need resiliency across process restarts. - -**Sample:** `python/samples/getting_started/workflows/checkpoint/checkpoint_with_human_in_the_loop.py` - -### Implementation Overview - -1. Enable checkpointing: - - ```python - storage = FileCheckpointStorage(storage_path="./checkpoints") - workflow = ( - WorkflowBuilder() - # ... add executors and edges ... - .with_checkpointing(checkpoint_storage=storage) - .build() - ) - agent = workflow.as_agent() - ``` - -2. Start the workflow with a `checkpoint_id` (often a session or job ID): - - ```python - checkpoint_id = "workflow-123" - response = await agent.run(user_prompt, checkpoint_id=checkpoint_id) - ``` - -3. Detect a pause by inspecting `FunctionCallContent` or the returned `WorkflowStatusEvent`. - -4. Persist the outstanding request and notify the reviewer (email, Teams, ticketing system, etc.). - -5. When the decision arrives, resume from the saved checkpoint: - - ```python - human_response = FunctionResultContent(call_id=call_id, result=decision) - response = await agent.run( - ChatMessage(role=Role.TOOL, contents=[human_response]), - checkpoint_id=checkpoint_id, - ) - ``` - - Alternatively, call `workflow.run_stream_from_checkpoint(...)` if you are driving the workflow directly rather than through `WorkflowAgent`. - -### Storage Options - -| Development | Production | -| --- | --- | -| `FileCheckpointStorage` | `RedisCheckpointStorage`, `CosmosDBCheckpointStorage` | - -**Tip:** The sample demonstrates `RequestInfoExecutor.pending_requests_from_checkpoint(...)` which makes it easy to pre-supply human answers before resuming a checkpoint. - -## Pattern 3: Event-Driven Architectures - -For high-scale systems, place a message bus between the workflow and reviewers. The workflow publishes a “human approval needed” event, then sleeps with its state checkpointed. A worker or API endpoint collects the human decision later and calls `agent.run(... checkpoint_id=...)` to resume. - -### Suggested Flow - -1. Workflow publishes `{session_id, function_call_payload}` to a queue or Event Grid topic. -2. Notification service surfaces the request to humans. -3. Reviewer submits a decision through a web app or chat bot. -4. API handler constructs `FunctionResultContent` and resumes the workflow using the stored `session_id`. - -This pattern avoids holding open network connections and makes it easy to scale out workflow runners and human-facing services independently. - -## Putting It Together: End-to-End Example - -```python -async def start_workflow(session_id: str, prompt: str): - response = await agent.run(prompt, checkpoint_id=session_id) - call = extract_request_info_call(response) - if call: - save_pending_request(session_id, call) - notify_manager(session_id, call) - return {"status": "pending", "session_id": session_id} - return {"status": "completed", "result": read_output(response)} - - -async def resume_workflow(session_id: str, decision: ReviewResponse): - call = load_pending_request(session_id) - result = FunctionResultContent(call_id=call.call_id, result=decision) - response = await agent.run( - ChatMessage(role=Role.TOOL, contents=[result]), - checkpoint_id=session_id, - ) - return {"status": "resumed", "result": read_output(response)} -``` - -## Best Practices - -- **Keep request payloads serializable.** Use dataclasses with primitive types so checkpoints can persist them reliably. -- **Include identifiers.** Store request IDs, user IDs, and draft previews to help humans decide quickly. -- **Set checkpoint TTLs.** Clean up abandoned sessions using the storage provider’s expiry features. -- **Audit every decision.** Persist the human response alongside the workflow output for compliance. -- **Stress-test resume logic.** Simulate process crashes by stopping and restarting the workflow runner before providing the human response. -- **Secure the channel.** Ensure that only authorized users can approve or reject requests. - -## Troubleshooting - -| Symptom | Likely Cause | Fix | -| --- | --- | --- | -| `ValueError: Human review request payload must be a mapping` | The function call payload was parsed without respecting the custom dataclass type. | Access `request.data` as the dataclass (`HumanReviewRequest`) and reference its fields directly. | -| Workflow does not pause | Request was not routed through `RequestInfoExecutor`. | Ensure `ctx.send_message(..., target_id=request_info_executor.id)` is used. | -| Workflow resumes but re-prompts for the same request | Decision was not supplied or `call_id` mismatch. | Pass the same `call_id` from the original `FunctionCallContent` when constructing `FunctionResultContent`. | -| No checkpoints saved | `WorkflowBuilder.with_checkpointing(...)` was not called or checkpoint storage misconfigured. | Configure storage and provide a unique `checkpoint_id` when running the workflow/agent. | - -## Additional Resources - - - -- **Samples** - - DEAD LINK `workflow_as_agent_human_in_the_loop_azure.py` ../python/samples/getting_started/workflows/agents/workflow_as_agent_human_in_the_loop_azure.py - - DEAD LINK `checkpoint_with_human_in_the_loop.py` ../python/samples/getting_started/workflows/checkpoint/checkpoint_with_human_in_the_loop.py -- **API References** - - DEAD LINK `RequestInfoExecutor` ../python/packages/core/agent_framework/_workflows/_request_info_executor.py - - DEAD LINK `WorkflowBuilder` ../python/packages/core/agent_framework/_workflows/_workflow.py - - DEAD LINK `WorkflowAgent` ../python/packages/core/agent_framework/_workflows/_workflow_agent.py - - -By combining `RequestInfoExecutor` with checkpoint-aware workflows, you can build resilient human-in-the-loop systems that pause safely, resume on demand, and provide a clear audit trail for every decision.