sadd-multi-agent-patterns

To design a multi-agent system, you partition complex tasks across specialized agents using supervisor, peer-to-peer, or hierarchical patterns. This architecture overcomes single-agent limitations by isolating context and enabling inter-agent communication for diverse sub-tasks.

Context isolation in multi-agent architectures partitions work and state across separate agents to prevent interference. You need it when complex tasks exceed a single agent's context window, requiring distributed information gathering and synthesis from diverse sources.

You coordinate multiple agents by implementing file-based memory and communication channels for inter-agent state management. Consensus mechanisms ensure agreement among distributed agents, while patterns like supervisor or orchestrator manage task routing and prevent divergence.

Common failure modes in distributed agent systems include communication bottlenecks and state divergence. You mitigate these issues using hierarchical coordination patterns, robust consensus mechanisms, and file-based memory to synchronize inter-agent state and manage task recovery.

Yes, you can use the supervisor pattern to coordinate specialized agents for code review tasks. The orchestrator distributes sub-tasks like analysis and synthesis across agents, managing context isolation and file-based communication to handle complex decomposition.

The best way to handle inter-agent state management without dependencies is using file-based memory and communication. This approach enables agents to share state, coordinate consensus, and maintain context isolation within distributed system architectures.