multi-agent-patterns

Multi-agent orchestration distributes tasks across multiple specialized agents to extend reasoning capacity, improving throughput and reliability for complex workflows. You define specialized agents, decompose objectives into subtasks, assign them, and monitor aggregated results.

Supervisor patterns use a central orchestrator for coordination and state management, peer-to-peer swarm patterns enable flexible distributed execution, and hierarchical patterns handle layered planning and execution for complex task decomposition.

Context isolation separates agent states to prevent interference, while failure recovery practices and consensus mechanisms ensure robust automation. These coordination protocols mitigate errors during distributed task execution and result aggregation.

Yes, you can implement multi-agent coordination using Python with example implementations and configuration guidance for LangGraph, AutoGen, and CrewAI. These frameworks support supervisor, peer-to-peer, and hierarchical execution patterns.

Use a peer-to-peer swarm for flexible, distributed task execution without a central bottleneck, and choose a central orchestrator when you need strict state management and coordinated handoffs for complex research projects.

Define a set of specialized agents and a supervisor, decompose user objectives into subtasks, and assign each subtask to the appropriate agent. Monitor, aggregate, and refine results using handoffs and consensus as needed.