multi-agent-patterns

Design multi-agent systems for complex task decomposition by applying architectural patterns like supervisor/orchestrator, peer-to-peer/swarm, and hierarchical structures to coordinate distributed LLM workflows efficiently. These patterns manage parallel processing and task routing.

The supervisor pattern in multi-agent orchestration is an architectural structure where a central orchestrator agent coordinates subordinate agents, managing handoffs and aggregating results to achieve complex task completion. It controls distributed LLM workflows.

Manage context isolation and token economics across multiple AI agents by distributing specific subtasks to individual agents, preventing context window overflow. This approach maintains isolation while enabling coordinated information passing.

Use peer-to-peer swarm patterns for decentralized consensus and parallel processing, while hierarchical structures suit tasks requiring top-down control and complex task decomposition. Choose based on your distributed LLM workflow coordination needs.

Achieve consensus and manage handoffs between AI agents by implementing coordination mechanisms defined within supervisor or swarm architectural patterns. These mechanisms structure information passing and result aggregation for distributed LLM workflows.

Yes, you can use multi-agent patterns for parallel processing scenarios with distributed LLM workflows. Architectural patterns like peer-to-peer/swarm and hierarchical structures specifically address complex task decomposition and concurrent execution coordination.