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Microsoft Agent Framework Fundamentals
Rating: 4.2 out of 5(28 ratings)
1,620 students

Microsoft Agent Framework Fundamentals

Start building agents with Microsoft Agent Framework
Last updated 11/2025
English

What you'll learn

  • Build and deploy functional AI agents using the Microsoft Agent Framework.
  • Implement long-term memory to allow agents to retain context across conversations.
  • Orchestrate multi-agent workflows where agents collaborate on complex tasks.
  • Apply observability, governance, and troubleshooting best practices for production.

Course content

4 sections19 lectures1h 57m total length
  • Introduction1:43
  • Setup Microsoft Agent Framework4:15
  • Build your first AI Agent10:13
  • MAF - Lab Enroll0:21
  • Course Setup: Accessing the Labs0:23
  • Hands-on Lab: Introduction to the MAF Agent Framework0:01
  • Lab Build Your First AI Agent11:20

Requirements

  • No prior experience required; everything you need will be covered.

Description

Unlock the potential of MAF in this immersive, hands-on module designed for developers who want to understand the framework from the inside out. Rather than focusing on high-level theory or complex production deployment strategies, this course prioritizes practical, tangible familiarity with the core building blocks of agent architecture. You will step into a "sandbox" environment where the primary method of discovery is active experimentation: running code, tweaking parameters, and directly observing how specific changes alter agent behavior.

The curriculum follows a logical technical progression, starting with the fundamentals of single-agent configuration. You will begin by initializing basic agents, manipulating their prompts, and analyzing execution logs to demystify their decision-making processes. Once comfortable with the basics, you will tackle the critical challenge of state management. Through targeted exercises, you will experiment with different memory architectures, contrasting short-lived session contexts against persisted state, and learn the mechanics of effectively resetting or restoring agent history.

As the module advances, the focus shifts to agency and interaction. You will learn to wire up function tools, enabling your agents to interact with external data through structured inputs and robust error handling. Finally, you will bring these elements together in a study of multi-agent orchestration. By routing tasks between distinct agents and managing handoffs, you will observe how simple coordination patterns can be composed to solve more complex problems. By the end of this workshop, you will possess a robust mental model of how MAF components interact, equipping you with the confidence to reliably run, debug, and modify your own agentic prototypes.

Who this course is for:

  • Python developers wanting to build autonomous AI agents.
  • AI Engineers looking to master multi-agent orchestration patterns.
  • Software architects seeking to understand AI governance and observability.