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Workshop for building intelligent AI solutions using Azure AI Foundry, featuring Vector Search, RAG, Agentic AI, and multi-agent orchestration with LangChain and Azure AI Search.
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This project is currently in active development and may contain breaking changes.
Updates and modifications are being made frequently, which may impact stability or functionality. This notice will be removed once development is complete and the project reaches a stable release.
This Azure AI Foundry Workshop provides participants with practical, hands-on experience in building and deploying Retrieval-Augmented Generation (RAG) and Agentic AI solutions using Azure AI Foundry, Azure AI Search, Azure AI Agent Service, LangGraph and Semantic Kernel Agent Framework.
Participants will learn how to create intelligent agents that not only respond, but also take action. Through integrating OpenAPI endpoints and orchestrating workflows across multiple agents, you’ll build solutions that are dynamic, context-aware, and production-ready.
By the end of the workshop, you'll have:
Vector Search & RAG with Azure AI Search
Learn to index documents, generate embeddings, and implement semantic retrieval to support LLM-based answers grounded in your data.
Agentic AI with LangGraph, Azure AI Agent Service and Semantic Kernel
Use prebuilt and custom agents to delegate tasks, make decisions, and interact with APIs. Experiment with orchestration patterns including single-agent flows, supervisor models, and decentralized networks.
Real-World Integrations with OpenAPI, GraphQL, and MCP
Connect your agents to external services to perform real-world actions like retrieving live data, triggering workflows, or interacting with apps and systems — leveraging Model Context Protocol (MCP) to define tools once and expose them consistently across scalable, modular architectures.
Follow these key steps to successfully implement and deploy the workshop:
Step-by-step instructions to deploy Azure AI Foundry and all required services for the workshop environment, including:
Step-by-step instructions for vectorizing document data with Azure AI Search and quickly leveraging the data using Azure AI Foundry's built-in Chat Playground.
Discover interactive Notebooks and guides that walk you through building intelligent, task-driven agents. These curated resources cover:
This solution combines the power of Azure AI Foundry, LangGraph, and the Azure AI Agents Service to build an advanced, modular AI orchestration framework. It demonstrates a production-ready architecture designed for scalable, intelligent applications that require real-time reasoning, search, and structured data integration.
At a high level, the architecture consists of the following key components:
The deployment includes Azure AI Foundry’s full stack—AI Hub, AI Services, AI Projects, and Compute Instances—providing a secure and managed environment for developing and running generative AI applications. Compute Instances are pre-configured to support Visual Studio Code (web), enabling a browser-based development experience for running and modifying sample notebooks directly within the Foundry environment.
Unstructured data, such as PDFs, are preprocessed through an Azure Function that chunks documents, generates vector embeddings using OpenAI’s Ada-002 model, and indexes them into Azure AI Search. This enables Retrieval-Augmented Generation (RAG) capabilities by grounding responses in your custom knowledge base.
Agents are orchestrated using LangGraph, a framework that enables complex workflows through node-based logic. A Supervisor Agent coordinates multiple specialized agents, allowing for role-based delegation, context-aware task execution, and adaptive reasoning.
To enable agents to interact with structured external data sources, the solution integrates tools via OpenAPI (for RESTful APIs) and GraphQL (for schema-based query interfaces). These tools extend agent capabilities, allowing them to fetch, query, or write to external systems dynamically during conversation.
Document processing is fully event-driven. When a PDF is uploaded to the designated storage container, an Azure Function is triggered to process the document end-to-end—from chunking to indexing—ensuring the search index remains up-to-date.
After completing the workshop and testing, ensure you delete any unused Azure resources or remove the entire Resource Group to avoid additional charges.
This project is licensed under the MIT License, granting permission for commercial and non-commercial use with proper attribution.
This workshop and demo application are intended for educational and demonstration purposes. It is provided "as-is" without any warranties, and users assume all responsibility for its use.