Multi-Agent Collaboration

Multi-agent collaboration studies how groups of AI agents divide work, communicate, coordinate actions, and combine evidence or expertise to solve tasks that may exceed a single agent’s abilities. Common approaches include role-specialized teams, learned or adaptive communication graphs, debate and consensus protocols, shared memory, and multi-agent reinforcement learning for settings such as ad hoc teamwork, software engineering, reasoning, and robotics. Researchers evaluate systems on task-specific benchmarks and increasingly measure collaboration traces, handoffs, information use, and robustness to unfamiliar or unreliable teammates. A central tension is that added agents and communication can improve coverage and verification but also increase cost, propagate errors, suppress useful disagreement, or create coordination bottlenecks.