multi-agent-patterns

Multi-agent system design patterns like supervisor, swarm, and hierarchical architectures coordinate multiple AI agents to solve complex tasks. You need them when task complexity or context requirements exceed a single agent's capabilities, necessitating parallel execution and inter-agent communication.

Design a multi-agent system by selecting an architectural pattern like supervisor or swarm, then isolating context for each agent to handle decomposed subtasks. This enables parallel execution and structured inter-agent communication to synthesize final results.

Supervisor architectures use a central orchestrator to manage agents, swarm patterns rely on peer-to-peer coordination without a central leader, and hierarchical patterns organize agents into nested tiers for scalable multi-agent system design and complex task delegation.

Context isolation assigns each agent its own dedicated context window, partitioning work so individual agents process specific subtasks independently. This prevents context overflow and allows parallel execution within the multi-agent system without cross-contamination of information.

You should avoid multi-agent architectures when tasks fit comfortably within a single agent's context window and reasoning capabilities. Implementing unnecessary orchestration, swarm, or hierarchical patterns adds inter-agent communication overhead and complexity without proportional benefits.