How an Orchestrator Keeps Multi-Agent AI Systems From Falling Apart

In multi-agent AI systems, an orchestrator acts as a central coordinator that prevents individual agents from operating without direction or cohesion. Its core responsibilities include decomposing high-level goals into subtasks, routing those tasks to appropriate agents, managing workflow state, and synthesizing final outputs. Crucially, the orchestrator itself never executes tasks like running code or calling APIs — it only reasons about work and delegates it. A minimal Python implementation demonstrates this separation by using a language model twice: once to plan and once to synthesize, while leaving all execution to worker agents. More advanced orchestrators also incorporate replanning logic to adapt when a worker returns an unexpected or failed result.