ai-coordinating-agents
Build Multi-Agent Systems
Guide the user through building multiple AI agents that collaborate — a supervisor delegates tasks, specialists handle their domains, and results flow back. Uses DSPy for each agent's reasoning and LangGraph for orchestration, handoff, and parallel execution.
Step 1: Identify the agents
Ask the user:
- What's the overall task? (research a topic, handle support, create content, analyze data?)
- What specialist roles do you need? (researcher, writer, reviewer, analyst, etc.)
- How do agents hand off work? (supervisor routes, chain passes forward, parallel fan-out?)
- Do any agents need tools? (search, database, APIs, code execution?)
Common multi-agent patterns
| Pattern | How it works | Good for |
|---|---|---|
| Supervisor | Central agent routes tasks to specialists | Support triage, research coordination |
| Chain | Agent A → Agent B → Agent C in sequence | Content pipelines (write → edit → review) |
| Parallel | Multiple agents work simultaneously, merge results | Research (search multiple sources at once) |
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