multi-agent-patterns

A multi-agent architecture coordinates multiple specialized LLM agents to execute complex tasks. You need it when single-agent context window limitations restrict task scale or when parallel execution and distributed AI orchestration are required.

Design a supervisor multi-agent pattern by configuring a central orchestrator to route subtasks and manage context isolation across specialized worker agents. This enables hierarchical coordination, task decomposition, and structured failure handling.

Implement context isolation by structuring distributed AI architectures that segment working memory per agent, preventing cross-agent token overflow. This ensures focused processing, optimizes token economics, and maximizes parallelization benefits across the orchestrated swarm.

Consensus mechanisms in peer-to-peer swarm systems enable distributed AI agents to communicate, debate, and vote on outputs. This coordination ensures aggregated results and fault tolerance when multiple agents collaborate on complex distributed tasks.

A hierarchical multi-agent pattern improves token economics by isolating context windows and parallelizing subtask execution. Distributing work across orchestrated sub-agents prevents context overflow and optimizes token usage compared to a single overloaded agent.

Limitations of peer-to-peer swarm patterns include complex failure handling, consensus overhead, and potential communication bottlenecks. Without a central orchestrator, managing context isolation and ensuring reliable consensus across distributed agents becomes challenging.