multi-agent-patterns

Multi-agent coordination distributes work across specialized agents using supervisor, peer-to-peer, or hierarchical patterns. A supervisor routes tasks to workers and synthesizes results; swarms enable flexible handoffs without bottlenecks; hierarchical setups separate strategic, planning, and execution layers for scalable problem-solving.

Supervisor patterns use centralized control to route tasks and aggregate results, creating a clear bottleneck. Swarm patterns enable peer-to-peer handoffs and emergent problem-solving without a single controller, offering flexibility and parallel exploration at the cost of complexity.

Context isolation and explicit handoffs preserve modularity by clearly defining what state and information each agent receives and passes. Structured messaging protocols and modular patterns enforce boundaries, reducing unintended information leakage between agents.

Yes. Multi-agent architectures include consensus mechanisms and coordination protocols for robust operations. These enforce agreement before proceeding and handle disagreement through structured failure recovery.

Partition work when a single agent hits context limits, tasks decompose into specialized subtasks across domains, or you need parallel exploration and richer reasoning. Multi-agent setups scale workflows by distributing cognitive load.

Multi-agent patterns include failure handling and structured failure recovery through modular coordination protocols. These define how agents retry, escalate, or compensate when subtasks fail.