Stanford study finds a single AI agent beats teams at managing shared resources

More AI agents should mean more brainpower. A new Stanford study suggests it often means more chaos instead. The paper, titled “Worse Together: How Performance Breaks Down in Multi-User Multi-Agent Teams,” finds that teams of AI agents serving different users consistently underperform a single coordinating agent when they share limited resources. The research was submitted to arXiv on September 30, 2026, under the identifier arXiv:2610.00583v1. What the researchers actually tested The environments include shared API-token budgets, clinic scheduling, personal-assistant bookings, and code-merge queues. The team evaluated five advanced models across 77 scenarios. To make the testing repeatable, they also introduced MAMUBench, a new benchmarking framework designed for standardized evaluation across three key environments. The researchers compared three basic team structures: Single coordinators: one agent manages requests on behalf of everyone. Peer-to-peer teams: each user gets an agent, and the agents can talk to each other. Silent teams: each user gets an agent, but the agents cannot communicate with peers. The numbers are not kind to teamwork Peer-to-peer teams reached only 12-30% ...