About the Instructor
Microsoft Research
AutoGen is developed by Microsoft Research (Chi Wang, Qingyun Wu and team). Published at NeurIPS 2023. Powers agentic coding assistants used by thousands of engineering teams globally.
First Principles
AutoGen models agents as conversational participants. An AssistantAgent proposes solutions; a UserProxyAgent executes code and reports back. The conversation loop continues until the task is solved — mirroring pair programming.
AI Ecosystem Fit
Layer 5 (Applications) — AutoGen is particularly powerful for code-heavy agentic tasks. When an agent can write, run, see results, and fix its own code, you get a closed feedback loop.
Prerequisites
What Paid Projects This Unlocks
Build self-correcting agent teams: automated coding assistants, multi-step problem solvers, agents that validate their own outputs.
3 Client Projects You Can Get Paid For
Real businesses have this problem right now. Build it, pitch it, charge for it.
Stack This With → (Unlocks Higher-Paying Projects)
Combine with RAG for knowledge-grounded coding agents
Deploy with Docker for safe code execution
Add human-in-the-loop checkpoints
Why This Matters
Self-correcting agents are 10x more reliable than single-shot agents. AutoGen's conversation model handles failures gracefully.