diff --git a/_data/sidebar.yml b/_data/sidebar.yml
index e3aacd894b..4b5d6df225 100644
--- a/_data/sidebar.yml
+++ b/_data/sidebar.yml
@@ -2042,3 +2042,32 @@
   isSectionHeader: 0
   sectionTitle:
   subgroup: 0
+
+#-------------- Prebid Agents --------------|
+
+- sbSecId: 10
+  title:
+  link:
+  isHeader: 0
+  isSectionHeader: 1
+  sectionTitle: Prebid Agents
+  sectionId: prebid-agents
+  subgroup: 1000
+  sbCollapseId: prebid-agents
+
+- sbSecId: 10
+  title: General
+  link:
+  isHeader: 1
+  headerId: agents-general
+  isSectionHeader: 0
+  sectionTitle:
+  subgroup: 0
+
+- sbSecId: 10
+  title: Salesagent
+  link: /agents/salesagent.html
+  isHeader: 0
+  isSectionHeader: 0
+  sectionTitle:
+  subgroup: 0
diff --git a/agents/salesagent.md b/agents/salesagent.md
new file mode 100644
index 0000000000..f849b35bf9
--- /dev/null
+++ b/agents/salesagent.md
@@ -0,0 +1,116 @@
+---
+layout: page_v2
+title: Salesagent
+description: A media sales agent that implements the AdCP Media Buy protocol
+sidebarType: 10
+---
+
+# Prebid Sales Agent
+
+The **Prebid Sales Agent** is a server that exposes advertising inventory to AI agents via the **Model Context Protocol (MCP)** and **Agent-to-Agent (A2A)** protocol. It is designed to integrate with ad servers like Google Ad Manager and provides tools for managing inventory and campaigns throughout their lifecycle.
+
+<div class="alert alert-info" role="alert">
+  For the full source code and latest updates, visit the <a href="https://github.com/prebid/salesagent">prebid/salesagent repository</a>.
+</div>
+
+## Key Features
+
+### For AI Agents
+
+- **Product Discovery**: Natural language search for advertising products.
+- **Campaign Creation**: Automated media buying with targeting capabilities.
+- **Creative Management**: Streamlined upload and approval workflows.
+- **Performance Monitoring**: Real-time access to campaign metrics.
+
+### For Publishers
+
+- **Multi-Tenant System**: Isolates data per publisher for security and organization.
+- **Adapter Pattern**: Supports multiple ad servers (e.g., Google Ad Manager).
+- **Real-time Dashboard**: Live activity feed powered by Server-Sent Events (SSE).
+- **Workflow Management**: Unified system for human-in-the-loop approvals.
+- **Admin Interface**: Web UI with Google OAuth for easy management.
+
+### For Developers
+
+- **MCP Protocol**: Standard interface for AI agents.
+- **A2A Protocol**: Agent-to-Agent communication via JSON-RPC 2.0.
+- **REST API**: Programmatic tenant management.
+- **Docker Deployment**: Easy setup for both local and production environments.
+
+## Getting Started
+
+### Quick Start (Evaluation)
+
+You can try the sales agent locally using Docker:
+
+```bash
+# Clone and start
+git clone https://github.com/prebid/salesagent.git
+cd salesagent
+docker compose up -d
+
+# Test the MCP interface
+uvx adcp http://localhost:8000/mcp/ --auth test-token list_tools
+```
+
+Access services at [http://localhost:8000](http://localhost:8000):
+
+- **Admin UI**: `/admin` (Test credentials: `test123`)
+- **MCP Server**: `/mcp/`
+- **A2A Server**: `/a2a`
+
+### Production Deployment
+
+For production, publishers can deploy their own sales agent instance. The repository provides guides for various deployment methods, including Docker and cloud platforms.
+
+## The AdContext Protocol (AdCP)
+
+The Sales Agent is built on the **AdContext Protocol (AdCP)**, an open standard designed to standardize how AI agents interact with advertising platforms.
+
+<div class="alert alert-info" role="alert">
+  For comprehensive documentation, visit <a href="https://docs.adcontextprotocol.org/docs/intro">docs.adcontextprotocol.org</a>.
+</div>
+
+### Protocol Architecture
+
+AdCP operates as a layer on top of standard AI interaction protocols:
+
+- **MCP (Model Context Protocol)**: Facilitates direct integration with AI assistants (e.g., Claude Desktop).
+- **A2A (Agent-to-Agent Protocol)**: Enables complex, autonomous workflows and collaboration between agents using JSON-RPC 2.0.
+
+### Core Concepts
+
+AdCP abstracts complex advertising operations into standardized domains:
+
+1. **Inventory Discovery** (`get_products`): Agents can search for ad products using natural language criteria (e.g., "video ads in North America") rather than specific line item IDs.
+2. **Media Buying** (`create_media_buy`): A normalized workflow for proposal, negotiation, and booking that works consistently across different ad servers.
+3. **Creative Management** (`build_creative`): Standardized handling of creative assets, allowing agents to generate or upload assets that match publisher specifications.
+4. **Signal Activation** (`get_signals`, `activate_signal`): Mechanisms for passing context and identity signals to improve targeting and campaign performance.
+
+### Workflow Example
+
+A typical AI-driven campaign flow using AdCP might look like this:
+
+1. **Discovery**: Expected outcome is a list of available "Products" matching the agent's intent.
+2. **Planning**: The agent uses `create_media_buy` to submit a proposal.
+3. **Review**: The Sales Agent (and potentially a human publisher) reviews the proposal.
+4. **Execution**: Once approved, the Sales Agent pushes the orders to the underlying ad server (e.g., GAM).
+
+## Architecture
+
+The project follows a clean structure isolating core MCP components, business logic services, and ad server adapters.
+
+```text
+salesagent/
+├── src/
+│   ├── core/           # Core MCP server components
+│   ├── services/       # Business logic services
+│   ├── adapters/       # Ad server integrations (e.g., GAM)
+│   └── admin/          # Admin UI (Flask)
+├── scripts/            # Utility and deployment scripts
+└── tests/              # Comprehensive test suite
+```
+
+## Contributing
+
+Contributions are welcome! Please refer to the [Development Guide](https://github.com/prebid/salesagent/blob/main/docs/development/README.md) in the repository for details on setting up your environment and creating pull requests.