OpenAI Researcher Noam Brown Says AI Agent Teams Have Clear Limits

In a newly published interview, OpenAI researcher Noam Brown argued that AI agent teams can cut waiting time on the right jobs, but their gains are uneven, less efficient than a single long-running system, and not yet well understood at the largest scales.

Brown described multi-agent systems as a response to a basic constraint in reasoning models. Letting one model think longer can improve an answer, he said, but eventually creates an impractical delay. A team of agents instead spreads test-time compute—the processing used while answering a task—across parallel workers. The tradeoff is that those workers do not share all the context a single agent would hold.

Brown said OpenAI’s published evaluations compared one, four, and 16 agents. On some benchmarks, four agents finished roughly twice as fast as one; 16 agents continued to improve performance, though with lower efficiency. He characterized the scaling as slightly sublinear, meaning each added agent tends to contribute less than the one before it.

That constraint is not merely financial. Brown said mathematics and research that requires reviewing many sources can be relatively easy to divide among agents. Writing a novel is likely far less suited to the approach, he said, because the work depends more heavily on a unified thread of context and judgment.

Brown said OpenAI lacks reliable science on how multi-agent systems scale to very large fleets because the needed comparison experiments are too expensive. Rather than claim a settled law from a few giant runs, he said researchers need systematic tests at scales such as 64, 128, and 256 agents to establish what extra agents actually contribute.

Brown also raised a longer-term constraint on reinforcement learning. As language models become more capable, finding tasks hard enough to teach them something new could become difficult. Unlike game systems that can continually face equally capable opponents through self-play, language-model training may eventually run short of sufficiently challenging problems. Brown called that a plausible scenario, not a wall OpenAI has reached, and said workarounds may exist.