Multi-Agent Systems: When One Isn't Enough
Complex tasks often need specialized agents working together. Here's how to design multi-agent architectures.
Why Multiple Agents?
Single agents try to do everything. This causes:
- Confusion about which tool to use
- Poor performance on specialized tasks
- Complex, hard-to-debug prompts
Multiple agents = specialization + coordination.
Common Patterns
1. Orchestrator + Workers
- Orchestrator — Receives requests, delegates tasks
- Workers — Specialized agents for specific domains
Example: Orchestrator routes to Research Agent, Writing Agent, or Code Agent.
2. Pipeline
- Agent A → Agent B → Agent C
- Each transforms the output
Example: Research → Draft → Edit → Publish.
3. Debate
- Multiple agents propose solutions
- Another agent evaluates and picks
Example: Three agents brainstorm, one selects best idea.
When to Use Multiple Agents
- Tasks need different expertise (code + writing)
- Workflow has distinct phases
- Single agent prompts become unmanageable
- Parallel processing saves time
Communication Between Agents
Agents need to share information:
- Shared memory — Common knowledge base
- Message passing — Direct communication
- Blackboard — Shared state object
Cost Considerations
Multiple agents = multiple LLM calls:
- Use cheaper models for simple subtasks
- Cache shared context
- Parallelize when possible