multi-agent-patterns

Multi-agent coordination patterns distribute work across specialized agents using supervisor, swarm, or hierarchical structures to overcome single language model context limits and enable parallel exploration.

You coordinate multi-agent tasks by specifying a coordination pattern like a central supervisor for orchestration or peer-to-peer swarm for exploration, which isolates context and handles failures across specialized agents working in concert.

Use hierarchical delegation for large, multi-step projects requiring nested task breakdown, while the supervisor pattern suits centralized orchestration across workers, and swarm coordination enables peer-to-peer exploration with explicit handoffs.

Yes, multi-agent workflows apply to cross-domain automation by assigning specialized toolsets to different agents, enabling robust failure handling and consensus mechanisms to satisfy complex enterprise workflow requirements.

Context isolation works by separating the operational state and reasoning boundaries of specialized agents, mitigating drift and errors while allowing parallel exploration across domains without exceeding a single model's context window.