multi-agent-patterns

To design a multi-agent system, use supervisor, peer-to-peer swarm, or hierarchical patterns to decompose complex tasks, enabling parallel execution and context isolation across distributed agents.

Context isolation in distributed AI distributes work across agents with individual context windows, preventing context degradation. You need it when single-agent systems lose coherence handling complex tasks.

Coordinate a supervisor agent with specialized agents using the supervisor or orchestrator pattern, where the supervisor delegates tasks like web searching, data analysis, and report synthesis to specialized agents.

Handling agent communication protocols and failure handling in swarm architecture requires understanding consensus mechanisms and failure strategies to maintain coordination within peer-to-peer distributed agent networks.

Yes, implementing hierarchical agents requires understanding agent communication protocols, consensus mechanisms, and failure handling strategies to ensure proper coordination and communication across the hierarchy.

Avoid multi-agent architecture when tasks fit within a single AI's capacity, as the overhead of agent coordination, communication protocols, and consensus mechanisms becomes unnecessary without complex task decomposition.