The Agent Innovator Lab is designed to provide a structured learning experience for AI agent development by leveraging Microsoft Azure's core services (Data & AI, App, and Infra). Each lab focuses on a specific topic, covering areas such as search algorithm optimization, agentic design patterns, and evaluation frameworks. Through this hands-on workshop, participants will gain practical experience in building, optimizing, and evaluating Azure-based AI agents, ultimately driving innovation and enhancing real-world AI system deployment. This repository includes RAG best practices, along with tools and techniques for innovating current architecture.
This hands-on lab is suitable for the following purposes:
- 1-day workshop (4-7 hours depending on customer)
- Hackathon starter code
- Reference guide for RAG/Multi-Agent design patterns
Provided below is a list of currently published modules:
| Title | Description and Link |
|---|---|
| Lab 0. Basic RAG | Create RAG application with Azure AI Search |
| Lab 0. Basic Agent | Basic Concepts of Agent and Agent toolkits (AutoGen and LangGraph) |
| Lab 1. Agentic Design Pattern | Practice representative patterns of Agentic RAG |
| Lab 2. Evaluation Design Pattern | Practice the Evaluation-Driven RAG patterns |
| Lab 3. Optimization Design Pattern | In Developmet |
Before starting, you should meet the following requirements:
-
Azure AI Foundry getting started: Create a project
-
[Evaluation driven LLMOps] Need to grant Storage File Data Privileged Contributor, Storage Blob Data Contributor at the storage of AI Foundry role to user, group, service principle and managed Identity which you are trying to access the data.
Please do not forget to modify the .env file to match your account. Rename sample.env to .env or copy and use it
git clone https://github.com/Azure/agent-innovator-lab.git
cd agent-innovator-lab
pip install -r requirements.txtgit clone https://github.com/Azure/agent-innovator-lab.git
cd agent-innovator-lab && conda activate azureml_py310_sdkv2
pip install -r requirements.txtThis 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 https://cla.opensource.microsoft.com.
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.
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