A multi-agent system is an AI setup where several agents, each with its own role, instructions and tools, work together on a larger task. Rather than one assistant doing everything, the work is divided among specialists.
A coordinating agent, or an orchestration layer, receives the request and passes parts to the right specialist. For example, one agent may handle product questions, another bookings and a third returns. They share relevant context and results, and the final answer is assembled for the customer or staff member.
In hospitality, one agent might manage reservations while another handles menu and allergen questions and a third deals with feedback. In retail, separate agents could cover stock enquiries, order tracking and marketing copy. Back-office examples include one agent preparing data and another checking it.
More agents add complexity, cost and more points of failure, and many problems are better solved with one well-designed agent. Ask whether the extra structure really helps, how the agents hand over, and how you would trace which agent made a particular decision. Also keep running costs in view, since every extra agent call uses processing time and usually adds fees.
It matters because as AI takes on broader jobs, splitting them into focused parts can improve accuracy and make each part easier to test and control.