Multi-agent systems are AI systems composed of multiple agents that collaborate, delegate, critique, route, or specialize across tasks. One agent might plan, another might retrieve, another might write, and another might evaluate.
Multi-agent systems increase capability, but they also increase failure surfaces. Evaluation needs to cover handoffs, conflicting decisions, duplicated work, communication failures, and cascading errors across agents.