Challenge 06 - Agentic AI
Pre-requisites
- Python 3.10+ installed
- Access to a Microsoft Foundry project with a deployed model (e.g.,
gpt-4o)
Introduction
Integrating agents into an application after implementing Retrieval-Augmented Generation (RAG) can significantly enhance user experience by providing personalized interactions and automating repetitive tasks. Additionally, agents can improve decision-making, ensure scalability, and offer real-time responses, making them ideal for complex task management and continuous improvement.
In this challenge, you will build a Research Assistant Agent using the Microsoft Agent Framework. This agent will leverage Model Context Protocol (MCP) to connect to live data sources like Microsoft Learn documentation, enabling it to provide accurate, up-to-date answers to technical questions.
Description
In this challenge, you will create a code-based agent that can query real-time documentation using MCP tools.
You will run the following Jupyter notebook to complete the tasks for this challenge:
CH-06-AgenticAI.ipynb
The file can be found in your Codespace under the /notebooks folder.
If you are working locally or in the Cloud, you can find it in the /notebooks folder of Resources.zip file.
The notebook covers the following areas:
- Setting up your environment and installing the Microsoft Agent Framework
- Creating the Research Assistant agent with MCP integration
- Testing single queries and multi-turn conversations
- Exploring how to extend the agent with custom tools
Test your agent with questions like:
- “What is Azure Kubernetes Service and when should I use it?”
- “How do I set up managed identity for Azure Functions?”
- “What are the best practices for Azure OpenAI prompt engineering?”
Success Criteria
To complete this challenge successfully, you should be able to:
- Demonstrate your understanding of what an agent is and how tools extend its capabilities
- Verify that your agent is created using the Microsoft Agent Framework in Python
- Verify that MCP tools are integrated to connect your agent to live data sources
- Demonstrate a multi-turn conversation with your Research Assistant
Tips
As you continue developing AI applications, consider how agents can be composed together—what coordination patterns would you use for multi-agent systems handling complex research or analysis tasks?
Clean-Up: Remember to delete your resource group in the Azure portal once you have completed all of the challenges.