multi-agent-patterns

Coordinate multi-agent AI architectures by partitioning tasks across specialized sub-agents using explicit handoff protocols, context isolation strategies, and framework compatibility with LangGraph, AutoGen, and CrewAI.

Multi-agent coordination relies on three core architectural patterns: Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical, combined with practical isolation strategies like full-context delegation, instruction passing, and file-system coordination.

Handle agent handoffs and failures in distributed AI workflows by implementing explicit handoff protocols, failure handling procedures, and consensus mechanisms to ensure reliable task execution and cross-domain knowledge sharing.

Yes, this multi-agent coordination approach provides framework compatibility with LangGraph, AutoGen, and CrewAI, allowing you to integrate supervisor, swarm, and hierarchical patterns into existing distributed systems.

Use hierarchical agent systems over peer-to-peer swarms when complex reasoning tasks require parallel subtasks, cross-domain knowledge, and scalable task execution with clear context boundaries and reliable handoff protocols.

Context isolation matters in multi-agent architectures because it ensures coordination protocols remain consistent, preventing specialized sub-agents from interfering with each other during complex reasoning and cross-domain task execution.