multi-agent-patterns

Multi-agent coordination overcomes single context limits by orchestrating multiple language-model agents to tackle complex tasks in parallel, enabling structured handoffs and cross-domain execution. It applies supervisor, swarm, and hierarchical patterns to synthesize results.

Structured handoffs during complex research are managed through a modular coordination framework implementing a handoff protocol and consensus management. This ensures seamless task execution and result synthesis across supervisor or peer-to-peer swarm patterns.

Hierarchical coordination is needed for large-scale data analysis when tasks require layered planning and execution. It orchestrates multiple agents to handle complex, cross-domain project coordination that exceeds a single agent's processing capacity.

Yes, the modular coordination framework can be extended with custom workers for parallel exploration. It implements a supervisor, handoff protocol, consensus management, and failure handling to support specialized task execution.

Fault-tolerance and consensus management in multi-agent orchestration work through a modular framework that handles failures and manages agreement among agents. This ensures coordinated reasoning continues reliably during complex cross-domain tasks.