Multi-agent systems use multiple specialized AI agents collaborating to complete complex tasks — each agent handles a specific role, just like a human team.

Common Multi-Agent Patterns

• Supervisor + Workers: One orchestrator delegates tasks to specialized sub-agents
• Pipeline: Output of one agent feeds into the next
• Debate: Multiple agents critique each other's outputs to improve quality
• Parallel: Multiple agents work simultaneously on different subtasks

Example: Research System

• Planner agent breaks down the research question
• Search agents gather information in parallel
• Analyst agent synthesizes findings
• Writer agent produces the final report
• Critic agent reviews and requests revisions

Reference:

Multi-agent tutorial

https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/