Multi-Agent Systems
Why Multiple Agents?
Single agents fail on complex tasks because: • Context window fills up • One agent can't be expert in everything • Parallel work is not possible • Hard to enforce specialization Multi-agent systems divide work across specialized agents that collaborate.
Patterns
Orchestrator-Worker: A planner agent breaks down tasks and delegates to specialist workers (researcher, coder, writer, critic). Peer-to-Peer: Agents communicate directly, each with their own goals. Supervisor: A supervisor reviews worker outputs, sends back for revision if quality is poor. Parallel: Multiple agents work independently on sub-tasks, results merged by coordinator.
Debate Pattern
Two agents argue for different solutions. A judge agent evaluates the debate and picks the winner. Forces exploration of multiple perspectives and surfaces weaknesses in reasoning.
Multi-Agent with LangGraph
LangGraph enables stateful, cyclical agent graphs with human-in-the-loop checkpoints — ideal for production agentic systems.
Finished reading? Mark it complete to earn your XP.