multi-agent-patterns

Multi-agent orchestration coordinates specialized sub-agents through supervisor, swarm, and hierarchical patterns with explicit handoff protocols and context isolation for complex task execution.

You orchestrate multiple AI agents by initiating a supervisor-led task decomposition, assigning subtasks to specialized agents, and monitoring handoffs and convergence to a final result.

Use hierarchical multi-agent orchestration for large research tasks, cross-domain projects, and production workflows requiring parallel exploration, domain specialization, and layered coordination.

Multi-agent orchestration supports production-grade workflows by providing robust failure handling, consensus coordination, and framework interoperability across distributed sub-agents.

Context isolation in a swarm is achieved by applying peer-to-peer coordination patterns with explicit handoff protocols, ensuring specialized sub-agents operate independently without context bleed.

Supervisor patterns coordinate cross-domain AI agents by distributing specialized subtasks and monitoring handoffs to converge diverse domain outputs into a unified final result.