multi-agent-patterns

Multi-agent architecture patterns include supervisor/orchestrator, peer-to-peer/swarm, and hierarchical models. They distribute tasks across specialized agents, enabling parallel execution and structured agent handoffs within frameworks like LangGraph, AutoGen, and CrewAI.

Context isolation in multi-agent systems separates each agent's working memory to prevent interference. Design guidelines specify techniques for maintaining clean contexts, ensuring agents process only relevant data during complex research workflows.

Agent handoffs in parallel execution environments are managed through defined architectural protocols. These guidelines describe how to facilitate seamless task transfers between agents across various multi-agent patterns like hierarchical or swarm.

Understanding multi-agent patterns requires knowledge of concurrency and distributed computing concepts. This foundational understanding is necessary to effectively implement parallel execution and manage complex agent coordination.

The best way to structure a multi-agent system for complex research projects is applying supervisor or hierarchical patterns. This ensures effective teamwork and context isolation among multiple collaborating specialists.