multi-agent-patterns

To orchestrate multiple AI agents, you can use architectural patterns like supervisor, swarm, or hierarchical models to coordinate specialized components, manage context isolation, and facilitate seamless inter-agent communication for complex problem-solving.

The best way to structure a multi-agent system depends on your needs: supervisor patterns for centralized control, peer-to-peer swarm intelligence for decentralized coordination, or hierarchical architectures for layered task delegation and context partitioning.

Multi-agent systems handle consensus and decision-making through explicit handoff protocols, weighted voting, and debate mechanisms, ensuring robust inter-agent communication and reliable aggregated outcomes across distributed AI components.

You need context isolation in AI agent coordination when scaling capabilities beyond single-agent limitations, allowing you to partition context so individual agents can process specialized tasks without interfering with each other's operational state.

Yes, you can use a hierarchical architecture to manage AI agent handoffs, enabling structured task delegation where a supervisor orchestrates subordinate agents, ensuring explicit handoff protocols and seamless context management.