In this hands-on lab, you’ll build a Knowledge Base using agentic RAG, the next evolution of retrieval in Azure AI Search. Connect your agentic retrieval engine to your data through smart source selection across multiple indexes and storage systems. Learn how to enhance planning using natural language guidance and generate grounded responses with citations or extractive answers tailored to your use case. By the end, you’ll have a fully functional Agentic Knowledge Base responds over enterprise data.
By the end of this session, learners will be able to:
- Design and build a Knowledge Base that uses agentic RAG to retrieve, reason, and respond over enterprise data.
- Implement smart source selection to connect and query multiple indexes and data sources intelligently.
- Use natural language guidance to enhance query planning and generate grounded, citation-rich, or extractive responses tailored to business needs.
- Azure AI Search
- Azure AI Foundry
- GitHub Codespaces / VS Code
The Microsoft Learn MCP Server is a remote MCP Server that enables clients like GitHub Copilot and other AI agents to bring trusted and up-to-date information directly from Microsoft's official documentation. Get started by using the one-click button above for VSCode or access the mcp.json file included in this repo.
For more information, setup instructions for other dev clients, and to post comments and questions, visit our Learn MCP Server GitHub repo at https://github.com/MicrosoftDocs/MCP. Find other MCP Servers to connect your agent to at https://mcp.azure.com.
Note: When you use the Learn MCP Server, you agree with Microsoft Learn and Microsoft API Terms of Use.
| Resources | Links | Description |
|---|---|---|
| Ignite 2025 Next Steps | https://aka.ms/Ignite25-Next-Steps | Links to all repos for AI Tour 26 Sessions |
| Azure AI Foundry Community Discord | Connect with the Azure AI Foundry Community! | |
| Learn at Ignite | https://aka.ms/LearnAtIgnite | Continue learning on Microsoft Learn |
 Ayca Bas 📢 |
 Matt Gotteiner 📢 |
 Pamela Fox 📢 |
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit Contributor License Agreements.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
