Welcome to the Video-Agents-Foundry-Solution, designed to help businesses leverage AI agents for automating complex video analysts tasks. This solution provides end-to-end framework for deploying AI-powered video analysis at the edge using Azure Video Indexer enabled by Azure Arc, combined with intelligent AI agents for automated decision-making and real-time video insights. The Video-Agents-Foundry-Solution allows users to process and analyze live and recorded video streams at the edge, extract AI insights, including speech transcription, OCR, face detection, object tracking, and action recognition and leverage AI agents to automate complex video analysis workflows with low latency and full data sovereignty.
Note: In order to successfully deploy the VI Extension, you must first submit your subscription for approval and be approved. Please use this application link. this form.
Deploying this solution can take approximately 45-60 minutes end-to-end (15-25 minutes for
azd upto complete, plus 20-35 minutes for GPU drivers and VI extension pods to fully initialize). During deployment, the Azure Portal may show some resources as "Failed" or "Unavailable". This is expected behavior as some resources may take longer to provision or initialize. Please allow up to an hour for all resources to be fully deployed and operational. If you continue to see issues after this time, please refer to the troubleshooting guide in the documentation.
Comprehensive Guides
For a detailed, step-by-step guide on creating an AKS cluster with GPU support and deploying the Video Indexer Arc extension, see:
- AKS-CLUSTER-SETUP.md - Complete setup guide with output examples
- create-aks-cluster.sh - Automated deployment script
The guide includes GPU quota checking, troubleshooting tips, and support for live video processing.
Note: With any AI solutions you create using these templates, you are responsible for assessing all associated risks and for complying with all applicable laws and safety standards. Learn more in the transparency documents for Agent Service and Agent Framework.
The solution leverages Azure Arc Service to extend Azure Video Indexer capabilities to edge environments running on Kubernetes clusters. By deploying the Video Indexer Arc extension on an Arc-enabled AKS cluster with GPU support (NVIDIA), the solution enables real-time video processing close to the data source — reducing latency and ensuring data sovereignty. AI agents built on Azure OpenAI orchestrate the video analysis pipeline, automating tasks such as content moderation, safety monitoring, and business intelligence extraction from video feeds. The agentic architecture allows multiple specialized agents to collaborate on complex video analysis tasks, with each agent handling a specific domain such as speech analysis, visual recognition, or compliance checking.
This solution optionally creates a Microsoft Foundry project and Foundry Tools (enabled by default; set CREATE_FOUNDRY_PROJECT=false to skip). More details about the resources can be found in the resources documentation.
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This template creates everything you need to get started with Microsoft Foundry (when CREATE_FOUNDRY_PROJECT=true, the default):
| Resource | Description |
|---|---|
| Azure AI Project | Optional - Provides a collaborative workspace for AI development with access to models, data, and compute resources |
| Azure OpenAI Service | Optional - Powers the AI model. Default model deployed is gpt-5.2, but any Azure AI model that supports tool calling can be specified per the documentation |
| Storage Account | Provides blob storage for application data and file uploads |
| Application Insights | Optional - Provides application performance monitoring, logging, and telemetry for debugging and optimization |
| Log Analytics Workspace | Optional - Collects and analyzes telemetry data for monitoring and troubleshooting |
Note: You should validate model availability in your region before deployment. The default model deployed with this solution is gpt-5.2, but you can customize the model and deployment configurations as needed. For more information on available models and their capabilities, please refer to the Azure OpenAI Service documentation: Foundry Models
- Azure Video Indexer enabled by Arc — Ignite 2025 Preview Blog
- Azure Video Indexer Documentation
- Azure Arc Documentation
- AKS Cluster Setup for Video Indexer Arc Extension
- Azure Video Indexer Samples on GitHub
For solutions to common deployment, container app, and agent issues, see the Troubleshooting Guide.
Learn more about the key features this solution enables
- Edge-Deployed Video Analysis — Run Azure Video Indexer on Arc-enabled Kubernetes clusters at the edge, bringing AI-powered video analysis closer to data sources for low-latency processing.
- Real-Time & Batch Video Processing — Support for both live video pipeline ingestion (RTSP streams) and batch processing of recorded video files.
- Customizable AI Model Deployment: The solution allows users to configure and deploy AI models, such as gpt-5.2, with options to adjust model capacity, deployment configurations, and knowledge retrieval methods.
- Multimodal AI Insights — Extract rich insights from video content including speech transcription, OCR, face detection, object tracking, scene detection, and action recognition.
- AI Agent–Based Automation — Leverage intelligent agents powered by Azure OpenAI to automate complex video analysis workflows, enabling automated decision-making and action triggering.
- Cloud-Managed Edge Deployments — Manage and monitor edge deployments centrally through Azure Arc while processing video data locally.
- Data Sovereignty & Compliance — Process sensitive video data on-premises or at the edge, ensuring data residency and compliance requirements are met.
- Extensible Agentic Architecture — Build custom agent teams for domain-specific video analysis scenarios such as retail analytics, manufacturing quality control, or safety monitoring.
Follow the quick deploy steps on the deployment guide to deploy this solution to your own Azure subscription.
Note: This solution accelerator requires Azure Developer CLI (azd) version 1.18.0 or higher. Please ensure you have the latest version installed before proceeding with deployment. Download azd here.
Click here to launch the deployment guide
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⚠️ Important: Check Azure OpenAI Quota Availability
To ensure sufficient quota is available in your subscription, please follow quota check instructions guide before you deploy the solution.
- Azure Subscription — An active Azure subscription. Create one for free.
- VI Arc Extension Approval — Submit your subscription for approval via this form before deploying.
- GPU Quota — Sufficient GPU quota for
Standard_NC4as_T4_v3VMs in your target region (minimum 4 vCPUs for the NCasv3 family). - Azure CLI — Version 2.x or higher. Install Azure CLI.
- kubectl — Kubernetes command-line tool. Install kubectl.
- Helm 3 — Kubernetes package manager. Install Helm.
- Azure Developer CLI (azd) — Version 1.18.0 or higher. Install azd.
Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. The majority of the Azure resources used in this infrastructure are on usage-based pricing tiers. However, Azure Container Registry has a fixed cost per registry per day.
Use the Azure pricing calculator to calculate the cost of this solution in your subscription. Review a sample pricing sheet for the architecture.
| Product | Description | Cost |
|---|---|---|
| Azure OpenAI Service | Powers the AI agents for task automation | Pricing |
| Azure Container Apps | Hosts the web application frontend | Pricing |
| Azure Cosmos DB | Stores metadata and processing results | Pricing |
| Azure Container Registry | Stores container images for deployment | Pricing |
⚠️ Important: To avoid unnecessary costs, remember to take down your app if it's no longer in use, either by deleting the resource group in the Portal or runningazd down.
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Learn more about what value this solution provides
- Real-Time Decision Making — Enable immediate, automated responses to events captured in video feeds, reducing the time from observation to action from hours or days to seconds.
- Reduced Operational Costs — Automate video monitoring and analysis tasks that previously required dedicated staff for manual review, significantly lowering labor costs.
- Edge Data Sovereignty — Process sensitive video data locally at the edge, ensuring compliance with data residency regulations and reducing bandwidth costs from streaming video to the cloud.
- Scalable Intelligence — Deploy consistent AI-powered video analysis across multiple locations (stores, factories, facilities) using a centralized cloud management model via Azure Arc.
- Actionable Insights — Transform raw video feeds into structured, actionable business intelligence through multimodal AI analysis and agent-driven automation.
Learn more about what use cases this solution provides
| Use Case | Persona | Challenges | Summary/Approach |
|---|---|---|---|
| Retail Analytics | Store Manager | Manual monitoring of customer behavior across multiple camera feeds is time-consuming and error-prone | AI agents analyze video feeds in real time for foot traffic patterns, shelf interaction, customer demographics, and queue lengths to optimize store layout and staffing |
| Manufacturing Quality Control | Quality Engineer | Visual inspection of production lines requires constant human attention and is subject to fatigue-related errors | Automated defect detection and quality monitoring via edge-deployed video analysis with AI agents that flag anomalies and trigger alerts in real time |
| Safety & Compliance Monitoring | Safety Officer | Reviewing security footage after incidents is reactive rather than preventive | Real-time safety monitoring with AI agents that detect PPE violations, unauthorized access, hazardous conditions, and automatically generate compliance reports |
| Smart City & Traffic Management | Urban Planner | Collecting and analyzing traffic data across a city requires expensive infrastructure and manual data processing | Edge-deployed video analysis at intersections provides real-time traffic flow data, incident detection, and pedestrian safety monitoring powered by AI agents |
This template also uses Managed Identity for local development and deployment.
To ensure continued best practices in your own repository, we recommend that anyone creating solutions based on our templates ensure that the GitHub secret scanning setting is enabled.
You may want to consider additional security measures, such as:
- Enabling Microsoft Defender for Cloud to secure your Azure resources.
- Protecting the Azure Container Apps instance with a firewall and/or Virtual Network.
Important Security Notice
This template, the application code and configuration it contains, has been built to showcase Microsoft Azure specific services and tools. We strongly advise our customers not to make this code part of their production environments without implementing or enabling additional security features.
For a more comprehensive list of best practices and security recommendations for Intelligent Applications, visit our official documentation.
Have questions, find a bug, or want to request a feature? Submit a new issue on this repo and we'll connect.
Please refer to Transparency FAQ for responsible AI transparency details of this solution accelerator.
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