multi-agent-patterns

Multi-agent architecture is a design pattern that distributes tasks across multiple specialized agents to enable parallelization and better context isolation. You need it to overcome single-agent limitations in complex task decomposition, distributed systems, and workflow automation.

Design a multi-agent system by applying architectural patterns like supervisor/orchestrator for centralized coordination, peer-to-peer/swarm for decentralized collaboration, or hierarchical structures for layered delegation. These patterns enable specialization and parallelization across distributed agents.

Context isolation prevents context pollution by maintaining separate context windows for each sub-agent. This ensures that specialized agents only process relevant information, avoiding interference and maintaining accuracy in complex multi-agent workflows.

Yes, you can implement consensus and coordination using weighted contribution protocols and debate protocols. These mechanisms ensure accurate outcomes by allowing multiple agents to evaluate, challenge, and validate results through structured coordination.

Common failure modes in multi-agent orchestration include context pollution, coordination deadlocks, and cascading errors. Mitigate them through context isolation, structured consensus protocols, and applying failure-aware design patterns available in the skill's references.

Manage multi-agent memory by separating session memory for active task coordination from long-term memory for persistent state. This approach maintains context isolation while enabling agents to share required historical information across complex workflows.