agent-supervisor-pattern

Multi-agent systems coordinate specialized AI agents by using a supervisor pattern, where a central supervisor agent manages workflow, assigns tasks to worker agents, and synthesizes their outputs for efficient execution.

The supervisor pattern in AI architecture is a hierarchical system design where a central supervisor agent routes and manages specialized worker agents. Use it for complex task orchestration requiring centralized workflow management and defined agent interfaces.

Agent orchestration uses defined interfaces and routing strategies to create sequential, parallel, or conditional execution paths. A supervisor agent breaks down tasks, assigns sub-tasks to workers, and manages state for robust execution.

Yes, a content generation system can be built using a supervisor agent that breaks down a topic, assigns research to a researcher agent, writing to a writer agent, and editing to a reviewer agent, then synthesizes the final output.

No specific dependencies are required to implement hierarchical system design. The supervisor pattern relies on defined interfaces for supervisor and worker agents, routing strategies, and state management to facilitate robust execution.

Centralized workflow automation via a supervisor agent requires robust state management and clear interface definitions to prevent routing bottlenecks. Complex conditional execution paths may introduce overhead in multi-agent systems communication.