multi-agent-patterns

Multi-agent patterns enable complex reasoning by distributing work across multiple agents. They address single-agent limitations through context isolation and coordination protocols like supervisor, peer-to-peer swarm, and hierarchical architectures.

Design a supervisor architecture by implementing an orchestrator agent that coordinates specialized sub-agents. This manages task decomposition and context isolation, allowing agents to retrieve, analyze, and synthesize information collaboratively.

Use hierarchical multi-agent architectures when tasks exceed single-agent context limits. They provide context isolation and structured task decomposition, enabling distributed systems to scale agent capabilities beyond single-agent constraints.

Context isolation works by designing agents to operate with focused context windows. This pattern overcomes single-agent limitations by ensuring each agent processes only the information needed for its specific task.

Failure modes in peer-to-peer swarm coordination involve breakdowns in consensus mechanisms and agent communication. Managing these requires robust state management and coordination protocols to prevent task execution failures.

Yes, understanding consensus mechanisms is required to build multi-agent systems. Designing coordination protocols and handling failure modes in distributed agent architectures depends on managing state across multiple agents.