Multi-Agent Systems
AI Engineer path · lesson 82 of 124Run an orchestrator that decomposes, delegates, and aggregates; compare supervisor / handoff / network topologies; see per-agent context isolation; and judge when multi-agent actually helps.
…assembled once all workers finish.
Key insight
Decompose → delegate → aggregate: specialized workers run in parallel and the orchestrator stitches the parts together. It buys parallelism and focus at the cost of coordination — use it when the task genuinely splits, not by default.
The supervisor pattern
Complete the multi-agent topology.
Tap a piece, then tap where it goes — or drag it. Tap the tray to take one back.
User request
Merged answer
Test your knowledge
What is the orchestrator's role in a multi-agent system?
The short version
A multi-agent system is a newsroom: the editor (orchestrator) assigns the reporter, the data analyst, and the copywriter, who work at once, then the editor stitches their pieces into one story.
In more depth
Multi-Agent Systems
A multi-agent system splits work across specialized agents coordinated by an orchestrator (supervisor): it decomposes the task, delegates subtasks to workers (often in parallel), and aggregates the results. Benefits: parallelism, separation of concerns, per-agent specialization (tools, prompts, models). Costs: coordination overhead, more tokens, harder debugging. Handoff patterns pass control sequentially; supervisor patterns fan out and back.
Interview Questions
References & Further Reading
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