multi-agent-patterns

Multi-agent systems use supervisor, peer-to-peer swarm, and hierarchical patterns. These architectures define how agents coordinate, delegate tasks, and manage communication to achieve complex AI capabilities beyond single-agent limitations.

Design a supervisor system by implementing an orchestrator that delegates specialized tasks to subordinate agents, manages handoffs, and aggregates results. This pattern enables structured multi-agent coordination and sequential task processing.

Context isolation in multi-agent systems manages individual context windows across agents to prevent overload and cross-contamination. This strategy ensures each agent operates within its specific domain knowledge and task parameters without interference.

Failure handling in multi-agent coordination uses circuit breakers and retry strategies to manage agent errors gracefully. These techniques prevent cascading failures and ensure system reliability during complex AI operations.

Choose a swarm intelligence pattern for decentralized peer-to-peer agent coordination without central control. Select hierarchical models when you need structured task delegation and supervised workflows with clear authority chains.

Consensus mechanisms in multi-agent systems enable collective decision-making through structured communication protocols. These mechanisms allow agents to share findings, negotiate outcomes, and coordinate handoffs for effective collaborative task completion.