multi-agent-patterns

Multi-agent coordination distributes work across multiple language model instances to overcome single-agent context limits, enabling parallel exploration and domain specialization for complex research and large-scale automation workflows.

To set up multi-agent coordination, define a user objective, instantiate a Supervisor agent, and route subtasks to specialist agents while collecting a final synthesis to manage complex workflows.

Available multi-agent patterns include centralized supervisor models, peer-to-peer swarms, and hierarchical structures, providing flexible coordination for complex research and multi-domain workflows.

Context isolation uses sub-agents to partition tasks, giving each language model instance its own context window to prevent drift and extend overall capability beyond a single model.

Multi-agent systems manage collaboration and reduce drift using explicit handoff protocols, consensus mechanisms, and robust failure handling through checkpointing, validation, and time-to-live constraints.

Yes, multi-agent coordination supports large-scale automation across tools by extending context and capability through distributed reasoning, domain specialization, and complex parallel task execution.