multi-agent-patterns

You coordinate multiple AI agents by implementing supervisor, swarm, or hierarchical architectures that partition context, delegate specialized subtasks, and manage distributed workflows. This enables parallel processing and complex problem-solving beyond single-agent capabilities.

The best way to isolate context in a multi-agent system is to partition information across specialized agents, preventing single-agent limitations. This requires explicit protocols for agent communication, handoffs, and consensus mechanisms to maintain workflow coherence.

Use a supervisor pattern when a central orchestrator must delegate tasks and coordinate specialized agents, whereas swarm architectures suit peer-to-peer distributed workflows. Both manage distributed AI workflows but differ in centralized control versus decentralized consensus.

Yes, explicit communication protocols are required for agent handoffs and consensus mechanisms in a hierarchical architecture. These protocols manage task delegation and ensure coordinated decision-making across specialized agents within the distributed system.

Yes, you can build a research system using the supervisor pattern to delegate tasks to specialized researcher, analyzer, and fact-checker agents. The supervisor coordinates their efforts to produce a comprehensive report through structured handoffs.

A single agent faces context limitations and lacks parallel processing capabilities, whereas a multi-agent swarm overcomes these constraints through context isolation and distributed coordination, though it requires explicit consensus mechanisms to manage the complexity.