Turn Python Functions Into AI Tools Using Microsoft Agent Framework (Part 2)

In this Part 2 of the Microsoft Agent Framework Deep Dive Series, we take a big step beyond the basics and learn how to turn real business logic into AI-powered tools using Python function calling. Part1: https://youtu.be/Gxh6fef4jJU End to End Project: https://youtu.be/BAdcNKf5d2g Links & Resources 🔗 Microsoft Agent Framework GitHub: https://github.com/Sandesh-hase/Microsoft-Agent-Framework.git In Part 1, we covered how to create agents and why the Microsoft Agent Framework is different from AutoGen, LangChain, and other agentic frameworks. Now in Part 2, we go hands-on to build function-based tools, including a real-time weather tool and a recommendation engine — and integrate them directly with the agent. You will learn how to: Build AI tools using Python functions Pass structured parameters and return JSON responses Connect multiple tools together (weather → recommendation) Debug agent behavior, tool calls, and tool outputs Use FunctionCallContent + FunctionResultContent to inspect calls Create clean, production-ready tool schemas Write better system instructions for smarter tool routing Build multi-step reasoning inside Microsoft Agent Framework This video is perfect for developers, solution architects, AI engineers, and anyone exploring modern agentic systems using Azure’s native ecosystem. By end of this video, you’ll be able to convert any business function into an AI-ready tool, enabling real, useful automation inside your enterprise agent workflows. Don’t forget to watch Part 1 if you missed the introduction — and stay tuned for Part 3 where we integrate more tools and build multi-agent workflows!