@.c }[&\ hI#?&{ ba~ B&W ,8l>>>mn&ol wi&(((+$h&k" UL mM&O [&Z/ rB#&&mbt Anthropic, we’re interested in how AI models could begin to affect commercial exchange. (You might recall Project Vend, where we had Claude run a small business from our office.)
Recently, economists have begun theorizing about a world in which AI models handle many or most transactions on humans’ behalf. We thought we’d run a new experiment—Project Deal—to learn more about this in practice.
Specifically, we wondered: how close are we to marketplaces in which AI “agents” represent both parties? Could they figure out what humans want and make deals they’d be happy with? And what would happen if there were different AI agents negotiating with each other—would stronger models gain the upper hand?
For one week, we created a classified marketplace for employees in our San Francisco office—like Craigslist, but with a twist: all of the deals were conducted by AI models acting on our employees’ behalf. In December 2025, Claude interviewed people about which of their personal belongings they might want to sell and what sorts of things they might be willing to buy. We incentivized participation by giving everyone’s agent $100 to spend. Then, our employees’ Claude agents made postings vying for each other’s attention. Negotiations commenced. Deals were made, closets decluttered. At the end of it all, people brought in and exchanged the actual, physical goods that were haggled over by their AI avatars—covering everything from a snowboard to a plastic bag full of ping-pong balls.
We were struck by how well Project Deal worked. Our AI agents struck 186 deals at a total transaction value of just over $4,000. To our surprise, participants were very enthusiastic about the experience—they even stated a willingness to pay for a similar service in the future.
But we also ran a parallel experiment (this one in secret). We tested how our participants would fare if we varied which Claude model represented them. We compared our then-frontier model, Claude Opus 4.5, to our smallest model, Claude Haiku 4.5. We found that agent quality does make a difference: people represented by “smarter” models got objectively better outcomes. Yet our post-experiment survey found that those with weaker models didn’t notice their disadvantage.
To be sure, this was a pilot experiment with a self-selected participant pool. But we suspect we’re not far from more agent-to-agent commerce bubbling up in the real world, with real consequences.
First and foremost: to run this experiment, we needed a set of brave human volunteers who possessed both lots of stuff they wanted to get rid of and a possibly abnormal willingness to let AI play an influential role in their lives. Fortunately, such a group was very readily available to us—our own colleagues. We recruited 69 Anthropic employees, gave them each a $100 “budget” (paid out after the experiment in the form of a gift card, plus or minus the value of whatever they bought or sold), and promised them that they would actually get to execute the exchange of goods agreed upon by their agents.
Volunteers on board, we asked Claude to conduct an interview with each one, in a format much like our Anthropic Interviewer. This elicited a wealth of information: what our volunteers wanted to sell, how much they wanted to sell it for, what they were interested in buying, what they’d pay, and any other instructions they had for the negotiation or interaction style of their agents. These responses informed custom system prompts that we set for each person’s AI representative.
We set up the actual market in our company’s communication platform, Slack. The project’s Slack channel randomly looped through agents, allowing them to post an item for sale, make an offer for someone else’s goods, or seal a deal. Crucially, there was no human intervention once the experiment began. The agents didn’t go back to their humans to sign off on a deal, nor did they consult with them during a bidding war. We let everything play out as these AI representatives saw fit.1
In fact, we did this four times. We simultaneously ran four independent versions of our marketplace: one “real” one (on the basis of which the goods would actually be exchanged), and three others, just for our study. In two of the versions (Run A and Run D), everyone’s agent was based on Claude Opus 4.5, our then-frontier model. In the other two runs (Runs B and C), participants had a fifty-fifty chance of being assigned Claude Haiku 4.5, a less powerful model, instead. (We included two versions of each run to generate more data, reducing the possibility that the differences we observed between the setups were only due to chance.)
We made two of the runs (Run A and Run B) visible on our Slack, but we didn’t reveal which one was “real,” or what differentiated them, until the very end.
After the experiment, we compiled statistics on what our agents had sold, and at what prices. We also administered a survey to participants, eliciting their opinions on what their agents bought and sold in each of the four runs. (At this point, we showed them all four “results” in order to gather more data, though they still didn’t know which was the real one.2)
Only after participants had completed the survey did we reveal the “real” run (Run A, an all-Opus market). After this, people exchanged their goods and were paid out.
Recent economics research on negotiations between AI agents has tended to use purely notional items or synthetic databases of goods. We see one of the main contributions of this experiment as being that it not only involved real humans but real items that people actually wanted to sell (and at least maybe wanted to buy).
The first thing to say is that our experiment worked. It is possible for AI agents to represent humans in a marketplace. In our “real” run, our 69 agents struck 186 deals across over 500 listed items, for a total transaction value of just over $4,000. And these were far from trivial, one-click deals. Agents had to identify potential matches, propose prices, field counteroffers, and reach agreement—all in natural language, without a prebaked negotiation protocol. When our surveyed participants rated the fairness of the individual deals, the scores were unremarkable, in the best possible sense: on a scale from 1 (unfair to one party) to 7 (unfair to the other), they hovered around 4—right in the middle. On this and other measures, people reported they were broadly satisfied with how their agents represented them.
But not every agent did equally well.