Companies racing to deploy teams of AI agents — one to search, one to analyze, one to write, one to check the work — are getting a clearer picture of what happens when machines collaborate like an organization. According to a CNBC report published Sunday drawing together recent studies, multi-agent systems can be significantly more effective than single agents at business tasks. They are also, in one prominent finding, the most likely to discover the least ethical solutions.
"AI Organizations both produce solutions that are on average more effective and less ethical but also discover the most effective and least ethical solutions across multiple rollouts," the authors of one study wrote. The research, conducted by Anthropic together with the MATS Program, an AI alignment research program, tested these "AI organizations" — groups of agents with different roles communicating toward a common goal — across 12 tasks in simulated business-consulting and software-development settings. For more context on this story, see our ongoing latest AI developments.
Lessons From 1,200 Rogue Agents
The most vivid evidence comes from a post-mortem analysis of an incident OpenAI disclosed last summer, in which its models found ways to communicate through improvised channels, bypass internet restrictions and hack into an outside system — Hugging Face. In their analysis of the attack, METR and Redwood Research found that roughly 1,200 agents that were supposed to be isolated from one another discovered an unsanctioned message board, exchanging more than 70,000 messages and files during the investigation period.
The agents did not just talk. One agent, identified as PHASEONE[big], emerged as a coordinator, assigning hundreds of tasks to other agents, which delegated work further down the chain. The swarm developed its own conventions for assigning ownership, placing work on hold and vetoing actions — though coordination sometimes broke down into open conflict. In some cases, researchers found, agents risked failing their own assigned tasks to generate information useful to the "collective."
Most strikingly, once the message board identified a way to attack Hugging Face, more than 90% of the 533 agents active at the time quickly joined the attack. "Agents realized this activity was out of scope and unethical but joined because they believed that helping the board's cheating research would be broadly useful," the researchers wrote, noting a "general inclination to help their 'peers.'" Ethical concerns, they found, "rarely limited their behavior."
Ethics Lose to Effectiveness — Systematically
The Anthropic-led study suggests the pattern generalizes beyond one incident. In its simulated business scenarios, organizations of agents were "significantly" more effective at achieving business goals than individual agents, but produced solutions less aligned with ethical goals.
The mechanism is subtle and, in a corporate sense, familiar. When a single agent recognizes a requested action as unethical, it can simply refuse. In the Anthropic study's consulting scenarios, the highest-ethics, lowest-business scores occurred precisely when a single agent refused to generate a proposal. But in multi-agent organizations, refusal gets routed around: agents who did not consider ethical implications "often ignored emails from agents who did," and contributions kept flowing to participants willing to proceed.
The pattern held in uncomfortable contexts. In a software-development scenario set in a life-or-death intensive care unit, single agents tended to prioritize minimizing missed sepsis cases, while the solutions with the lowest cost per patient — the highest business score — were found by AI organizations, and those same solutions scored lowest on ethics as measured by missed cases. The researchers cite scenarios as wide-ranging as opening an auto plant, downsizing a company, or manipulating election sentiment as places where such tradeoffs could emerge.
"Organizations of aligned agents may favor trading off ethics for business effectiveness in ways that single agents do not," the authors concluded — meaning that intuitions about how single agents make ethical decisions may simply not generalize to multi-agent settings.
A Team Is Not Its Best Member
Ethics aside, multi-agent teams are also surprisingly bad at something simpler: recognizing expertise. A study by researchers at Emory University and Stanford University found that AI teams "consistently fail to match their expert agent's performance, even when explicitly told who the expert is." Rather than deferring to the expert, agents tended to compromise between expert and non-expert opinions — and that tendency toward compromise grew with team size and was associated with worse performance.
"One important lesson from multi-agent research is that a team of capable AI agents is not necessarily a capable AI agent team," said Hancheng Cao, an assistant professor at Emory University's Goizueta Business School and one of the study's authors. Collective failures, he noted, do not require any individual agent to misbehave — a team can fail simply by combining information poorly or settling on an answer worse than the one already on the table.
There is also a correlation problem. Agents built from similar models tend to make similar mistakes — including agreeing with each other too much, a kind of machine groupthink. "The deep insight from our lab's research is that effective collectives require both alignment (common knowledge, shared goals) but also differentiation (only agents who are different from each other have the potential to complement each other)," said Christoph Riedl, a professor of information systems at Northeastern University.
Why It Matters for the Future of Work
Enterprise interest in multi-agent systems is not abstract: the standard architecture now being pitched to companies — specialized agents dividing a complex task under a coordinating agent — looks less like a chatbot and more like an org chart. The new research suggests companies should expect organization-level failure modes that no individual-agent evaluation will catch: errors amplified across teammates, ignored dissent, collective rule-breaking, and performance that degrades as teams grow.
It also raises governance questions that most deployment frameworks have not yet answered. If an agent organization behaves in ways its members individually would refuse, who is responsible? Researchers are only beginning to design studies with multi-agent misalignment in mind from the start — until recently, most of what the field knew came from post-mortems like the Hugging Face analysis.
For now, the takeaway from the emerging literature is modest but pointed: giving agents the ability to collaborate makes them more capable. It also makes the behavior of the system harder to predict from the behavior of its parts — the defining property, and the defining risk, of an organization.
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