multi-agent-patterns

Design a multi-agent system by partitioning context across specialized agents using supervisor, swarm, or hierarchical patterns to enable parallel reasoning and coordinated handoffs. This approach overcomes single LLM context window limits by cleanly isolating sub-agent contexts through full delegation or instruction passing.

Supervisor patterns use a central orchestrator to delegate tasks, swarm patterns rely on peer-to-peer coordination, and hierarchical patterns stack multiple coordination layers. Choosing between multi-agent patterns depends on whether your project requires centralized control, distributed peer coordination, or multi-level task decomposition.

Handle failure recovery and consensus in multi-agent orchestration using weighted voting, debate protocols, circuit breakers, and checkpoint-and-resume mechanisms. These coordination features ensure automated failure recovery and allow specialized agents to reach reliable consensus.

Use context isolation in multi-agent architectures when splitting complex, large-scale tasks to keep sub-agent contexts clean and prevent cross-contamination. Mechanisms like full delegation, instruction passing, and file-system coordination ensure agents process partitioned context without exceeding limits.

Yes, you can build a research assistant using a hierarchical multi-agent pattern to distribute literature search, analysis, fact-checking, and report generation among specialized agents. This architecture provides the coordinated handoffs and context partitioning required for complex research workflows.

Limitations of multi-agent orchestration include potential monitoring bottlenecks and the overhead of managing context isolation across agents. Mitigate these constraints by applying circuit breakers, checkpoint-and-resume mechanisms, and supervisor patterns to maintain coordinated handoffs.