multi-agent-patterns

To coordinate multiple AI agents, decompose a user task, route subtasks to specialized workers via supervisor or swarm patterns, and synthesize the final results. This explicit task decomposition and handoff protocol ensures scalable, distributed workflows.

Supervisor patterns use a centralized orchestrator for task synthesis and control, while swarm patterns enable peer-to-peer flexible handoffs and parallel exploration. Both manage multi-agent coordination but differ in centralized versus decentralized control flow.

Context isolation in multi-agent systems is managed through various delegation strategies that separate worker states. This core design principle prevents context bleeding during cross-domain coordination across research, writing, analysis, and execution phases.

Yes, hierarchical multi-agent patterns separate strategy, planning, and execution into distinct layers. This architecture allows specialized agents to handle specific execution phases while higher-level agents manage overall cross-domain coordination and task routing.

Robust multi-agent coordination implements explicit failure handling, consensus mechanisms, and convergence checks. These protocols ensure resilient distributed workflows by managing agent handoffs and verifying task completion across the swarm or supervisor network.