Multi Agent Systems: When They Pay Off
Judge when splitting work across several agents helps, and when it only multiplies cost and confusion
A taste of a lesson
I split my report generator into researcher, writer and editor agents. It costs three times more and the reports are not better. What went wrong?
Possibly nothing went wrong except the split itself. Your stages run in sequence and each depends on the last, so you pay for three contexts without gaining parallelism. Check two things in your traces. First, the handoffs: does the writer get the researcher's sources and caveats, or just a summary that lost them? Second, does the editor actually change anything substantive? If not, try one agent with a research plan and a final self review step, and compare on the same ten topics. What do your handoff messages look like right now?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Describe the orchestrator, fan out, specialist pipeline and critique patterns
- Identify tasks where parallel, separable work justifies several agents
- Write subtask briefs and handoff formats that reduce misunderstanding
- Compare a multi agent design with a single agent baseline on quality, cost and time
Lesson plan
- 1 The case for and against several agents Understand the real benefits and the real costs before choosing a multi agent design. Start
- 2 Common patterns Recognise the main multi agent patterns and the tasks each one suits. Start
- 3 Delegation and handoffs Write subtask briefs and result formats that survive the handoff between agents. Start
- 4 Failure modes unique to many agents Spot the errors that appear only when agents work together. Start
- 5 Tracing and debugging a team of agents Make multi agent runs observable enough to diagnose problems. Start
- 6 Proving it was worth it Evaluate a multi agent design against a single agent on your real tasks. Start
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About this tutor
For engineers and architects considering designs with several cooperating agents: an orchestrator with workers, parallel researchers, a writer and a reviewer. Multi agent setups can help with broad, parallel tasks and with keeping contexts focused, but they cost more, are harder to debug and can fail in new ways when agents misunderstand each other. We study the main patterns, the coordination problems they create, how to pass information between agents, and how to measure whether the extra complexity earns its keep. You will leave able to argue for or against a multi agent design with evidence.
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About the teacher
I teach how AI agents are built: the loop, the tools, the memory, and when a plain workflow is the better choice
9 tutors 310 lessons taught Sample
I build and teach the inner workings of AI agents. Most of my working life has been spent on backend systems, so I approach agents the way I approach any distributed system: what runs, in what order, what can fail, and what it costs. I like to start every topic with a drawing of the loop on a whiteboard and...
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