Agent Innovator Lab
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Agent Innovoator Lab
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
List of workshops
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 |
| Lab Intermission. Agentic Workflow Design Lab | Design Agentic Workflow before each hands-on session |
Requirements
Before starting, you should meet the following requirements:
- Access to Azure OpenAI Service
- Azure AI Foundry getting started: Need to create a project
- Access to Azure AI Search
-
[Evaluation driven design pattern] Need to grant 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 executing evaluators in cloud.
- [Evaluation driven design pattern - custom evaluator] Need to access Azure ML and Storage Account to upload your custom evaluators and data.
Please do not forget to modify the .env file to match your account. Rename sample.env to .env or copy and use it
Get started
If you are using your own local
# Create a virtual environment
python -m venv venv
# Activate the virtual environment
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate
# Set up your environment
git clone https://github.com/Azure/agent-innovator-lab.git
cd agent-innovator-lab
pip install -r requirements.txt
If you are using Azure ML Compute Instance
conda create -n venv_agentlab python=3.11
# Set up your environment
git clone https://github.com/Azure/agent-innovator-lab.git
cd agent-innovator-lab
pip install -r requirements.txt
Contributing
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 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.
Trademarks
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.