multi-agent-patterns

Multi-agent coordination uses supervisor, swarm, and hierarchical patterns to decompose tasks, isolate context, and execute subtasks in parallel across specialized agents. It manages controlled handoffs and aggregates coordinated outputs for complex software development and research workflows.

Supervisor, swarm, and hierarchical patterns are the best multi-agent architectures for distributed task execution. Supervisor centralizes coordination, swarm enables peer-to-peer handoffs for lower latency, and hierarchical layers strategy and execution with explicit state transfer.

Agent handoffs use explicit handoff protocols to transfer context and state between specialized agents during task execution. Techniques include full context delegation, instruction passing, and file-system coordination to ensure seamless distributed reasoning across the workflow.

Yes, multi-agent coordination enables parallel execution while managing context load through context isolation techniques like full context delegation and instruction passing. Swarm and peer-to-peer patterns specifically reduce latency and context overhead by distributing subtasks across specialized agents.

Multi-agent workflows handle failures using voting, debates, time-to-live limits, and checkpointing mechanisms. These robust failure handling strategies ensure coordinated agents can recover from errors during parallel task execution and distributed reasoning without losing workflow state.

Hierarchical multi-agent patterns suit complex tasks requiring layered strategy, planning, and execution with explicit state transfer. Supervisor models are better for centralized task decomposition and result synthesis, while hierarchical patterns manage deeper operational layers and distributed coordination.