multi-agent-patterns

You can coordinate multiple AI agents by implementing architectural patterns like supervisor, swarm, and hierarchical structures. This facilitates context isolation, inter-agent communication, and consensus mechanisms to solve complex problems beyond single-agent capabilities.

Common multi-agent architecture patterns include Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical structures. These patterns enable distributed intelligence and context partitioning to overcome single-agent limitations.

Context isolation in a multi-agent system is managed through context partitioning mechanisms provided by the architectural patterns. This overcomes single-agent limitations by ensuring specialized agents handle specific contexts independently.

Use a supervisor agent when you need to delegate tasks to specialized agents, such as a research system where a supervisor coordinates researcher, analyzer, and fact-checker agents to produce a comprehensive report.

Yes, building a multi-agent system requires understanding of agent coordination protocols and state management to effectively implement inter-agent communication, handoffs, and consensus mechanisms.

Single AI agents face limitations in complex problem-solving due to restricted context and capabilities, whereas swarm intelligence distributes tasks across specialized agents with context isolation for more robust solutions.