multi-agent-patterns

Multi-agent coordination patterns distribute reasoning across specialized agents using supervisor, swarm, and hierarchical models. They provide explicit context isolation and handoff protocols to manage complex research and cross-domain workflows where centralized coordination prevents drift.

To design a multi-agent pattern, outline the coordination steps and define a handoff protocol for your specific task. Select a supervisor, swarm, or hierarchical structure based on whether your workflow needs centralized control or distributed consensus.

Use a hierarchical pattern when subtasks require centralized supervisor control and structured state transfer. Choose a swarm pattern when specialized agents benefit from distributed consensus mechanisms and collaborative convergence without strict top-down orchestration.

Context isolation prevents drift by maintaining explicit boundaries around each specialized agent's state. This ensures that state transfer and convergence checks remain controlled during handoffs, keeping distributed reasoning aligned across cross-domain workflows.

Yes, robust failure handling can be implemented within multi-agent handoff protocols. The coordination framework includes explicit state transfer and convergence checks, ensuring that distributed agents recover gracefully and maintain workflow integrity during failures.

A centralized orchestrator is not strictly required. While supervisor and hierarchical patterns use centralized coordination, swarm patterns enable distributed agents to operate collaboratively using consensus mechanisms without a central orchestrator.