sadd:multi-agent-patterns

Multi-agent architecture patterns mitigate single-agent context bottlenecks by distributing subtasks across specialist agents, enabling parallel research and complex planning workflows through task decomposition and context isolation.

You design multi-agent workflows with context isolation by partitioning work so each agent operates in focused context, coordinating through shared files as memory and state, and using structured handoffs via state and result files.

You need consensus and review protocols in agent coordination when applying voting safeguards, debate stages, and trigger-based interventions to handle divergence, stalling, or sycophancy across specialist agents during complex planning.

The best way to handle failure modes like error propagation in multi-agent systems involves implementing checkpoints, validation, retries, and iteration limits to manage bottlenecks, coordination overhead, and divergence.

Yes, this multi-agent coordination approach supports supervisor and orchestrator, peer-to-peer and swarm, and hierarchical structures, allowing you to choose the architecture pattern that best fits your specific problem shape.

File-based memory and state management patterns for coordinating multiple agents include deciding between working, session, long-term, and entity or temporal memory patterns to facilitate structured handoffs and shared state.