Multi-agent systems route work to specialised agents to improve quality and parallelism. Three patterns: hierarchical (orchestrator delegates to worker agents), parallel (multiple agents work simultaneously on sub-tasks), pipeline (agent output feeds next agent). Agents communicate through handoff messages (conversational), shared state (key-value store), or message queues. Key challenges: context sharing, error propagation, and preventing agents from working at cross-purposes.
Structured handoff message between orchestrator and sub-agent.
Specialised agents with focused system prompts and curated tools outperform a single general agent on complex tasks. A Research Agent with web search tools beats a general agent for research sub-tasks.
A 4-task sequential workflow might take 40s. If 3 tasks are independent, parallel execution takes ~20s. Model the dependency graph explicitly and run independent branches concurrently.
In multi-agent systems, some sub-agents will fail. Design the orchestrator to handle partial results gracefully — continue with available data and note missing information rather than aborting.
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