5 Patterns for Coordinating Multiple AI Agents

5 Patterns for Coordinating Multiple AI Agents When you have one agent, you have a chatbot. When you have five agents, you have a coordination problem. The difference between chaos and function is how agents talk to each other. These five patterns are the scaffolding that separates useful multi-agent systems from ones that devolve into hallucination feedback loops. 1. Supervisor-Worker Pattern One agent orchestrates. Many agents execute. The supervisor agent receives a high-level task, breaks it into subtasks, spawns workers specialized for each piece, collects their outputs, and synthesizes a result. This is the most direct coordination pattern because control never leaves the supervisor. When to use : Document analysis, customer support triage, multi-step research where you control the flow. Example : class Supervisor : def process ( self , task ): subtasks = self . decompose ( task ) results = [] for subtask in subtasks : worker = self . spawn_worker ( subtask . type ) result = work