That is not a disclaimer — it is part of the point. The slides and the voice narrating the lectures were produced with AI tools, but every script was written and fact-checked by a human instructor with real hands-on experience. Think of the AI as the production crew; the teaching is human. When one agent isn’t enough, you build a team. A researcher, a writer, a critic, a manager, all collaborating. It’s powerful. It’s trendy. And it is genuinely easy to overdo — a team is a small organisation, with all the overhead that implies. This course is the honest guide to doing it right.
What you will learn:
- Multi-Agent Systems is a clear guide to how AI agents work together.
- You’ll understand the core topologies — manager-workers, pipeline, parallel, debate, and hierarchical. How agents communicate, how to design sharp roles, and how to orchestrate the whole thing.
- And crucially, the failure modes unique to teams, and how to know when a team beats a single agent — and when it doesn’t.
What this course covers, module by module:
Why Multi-Agent?; What Is a Multi-Agent System?; Manager–Workers; Sequential Pipeline; Parallel; Debate & Reflection; Hierarchical & Decentralized; Communication & Shared State; Designing Roles; Orchestration & Control; Failure Modes; When NOT to Use It; Building & Evaluating.
