AI Agents & LLMs 2026: Multi-Agent Systems

About this course

This course contains the use of artificial intelligence.

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.

Who this is for:

• This is for builders who've made a single agent work, and now face jobs too big for one.
• Let's build a team that actually works.
• By the end, you'll understand how to design teams deliberately, coordinate them cleanly, debug them, and earn the complexity instead of assuming it.
• Enroll now.

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