multi-agent-patterns

You coordinate multiple agents by distributing work, isolating context, and synchronizing results using supervisor, swarm, and hierarchical patterns. This approach enables parallel agents to handle research, planning, content creation, and data synthesis effectively.

The supervisor pattern uses stateful routing to coordinate workers, the swarm pattern enables direct peer-to-peer handoffs without a central bottleneck, and the hierarchical pattern layers strategy, planning, and execution for scalable complex tasks.

You need context isolation when distributing work across parallel agents to prevent state interference and ensure clean synchronization. It ensures specialized agents handle subtasks independently before synchronizing their final results.

You set up a basic multi-agent workflow by decomposing a complex task and assigning subtasks to specialized agents. The workflow uses handoff protocols and consensus mechanisms to enable direct peer-to-peer parallel exploration.

Yes, the peer-to-peer swarm pattern enables direct handoffs and parallel exploration without a central bottleneck. Agents coordinate autonomously, bypassing the stateful routing required by traditional supervisor or orchestrator architectures.

Hierarchical coordination layers strategy, planning, and execution to scale complex tasks, but requires careful synchronization of results across layers. If consensus mechanisms fail, context isolation between execution and strategy layers may break down.