multi-agent-patterns

Multi-agent coordination distributes tasks across specialized agents with isolated contexts, using explicit handoffs and supervision to synthesize results. This enables complex research and execution workflows that exceed single-agent capacity.

Context isolation assigns dedicated contexts to individual agents for specific sub-tasks. This reduces single-agent bottlenecks and expands capacity for complex reasoning by preventing context overflow across the distributed system.

Fault tolerance in multi-agent coordination is implemented through timeouts and checkpointing. These mechanisms handle failures during task distribution, ensuring robust execution and recovery across centralized, peer-to-peer, or hierarchical patterns.

Consensus mechanisms are needed when multiple specialized agents must agree on synthesized results during complex research or planning workflows. They ensure coordinated agreement before final output generation in distributed execution.

Yes, multi-agent architectures support centralized supervision, peer-to-peer swarms, and hierarchical coordination patterns simultaneously. You can route subtasks across these patterns to optimize task decomposition and result synthesis.

Task decomposition for multi-agent execution involves proposing a complex task, specifying subtask breakdown, and routing components to specialized agents. This distributes reasoning across isolated contexts with explicit handoff protocols.