multi-agent-patterns

You can coordinate multiple agents by using a Supervisor pattern to centrally plan and delegate subtasks to specialized subagents via the Task tool. This orchestrator approach partitions work into isolated contexts, preventing single-agent bottlenecks and enabling scalable task decomposition.

Effective multi-agent patterns include Supervisor for centralized planning, Peer-to-Peer for flexible handoffs, and Hierarchical for layered abstraction. These patterns enforce context isolation and use file-system memory for shared state, ensuring scalable and reliable software design.

Context isolation in multi-agent architectures works by partitioning work across separate agent invocations. This prevents single-agent context bottlenecks, enabling scale and specialization while using file-system memory to maintain shared state across isolated execution environments.

Yes, you can use Peer-to-Peer or Swarm patterns for flexible handoffs and collaborative exploration without a central bottleneck. These multi-agent patterns enable agents to interact directly, distributing workloads across diverse domains efficiently.

You should avoid hierarchical multi-agent architectures when tasks lack layered abstraction requirements or clear interfaces. If a task does not benefit from specialized subagents or centralized orchestration, simpler single-agent execution may be more efficient.