Multi-Agent System

A multi-agent system is an AI architecture in which several specialized agents work together on one goal. Instead of asking one model to plan, research, execute, check and report every step, the system splits work across roles: a planner decomposes the task, research agents gather context, coding or data agents take action, and reviewer agents validate the result. The key feature is not simply having many agents; it is the coordination layer between them. That layer defines task handoffs, shared state, tool permissions, failure handling, cost controls and stop conditions. Multi-agent systems become useful when a workflow is too complex for a single prompt or a linear automation. They can run work in parallel, route steps to different models based on capability or price, and cross-check outputs before humans see them. In production, however, they need a disciplined runtime with logging, observability, permissions and human approval points. Without those controls, a multi-agent setup can quickly become expensive, hard to debug and operationally unsafe.