16 · Multi-Agent Patterns

This designs AI teams that solve real enterprise problems using multi-agent patterns. Complex tasks are solved better by a team of specialized agents working together.

How it works

1. 1Supervisor pattern: one planner delegates tasks to specialized workers.
2. 2Parallel pattern: break work into subtasks and run agents in parallel, then synthesize.
3. 3Sequential (pipeline) pattern: each agent's output feeds the next.
4. 4Debate/reflection pattern: agents critique each other to improve the final answer.

Key points

• LangGraph defines agent nodes, edges and state; AgentCore secures and scales them.
• Give each agent a clear role and use structured output between them.
• Log and evaluate every agent with LangSmith.
• Start simple (supervisor) and add complexity only when needed.

Why it matters

Multi-agent systems tackle problems a single agent can't — combining specialists for research, analysis, coding and review with higher quality and speed.

Frequently asked questions

When do I need multiple agents?

For complex, multi-domain goals that benefit from specialists; simple tasks are better with one agent.

What's the safest starting pattern?

The supervisor pattern — one planner delegating to a few specialists — then evolve from there.