Project Deal: our Claude-run marketplace experiment

Source: Kevin K. Troy, Dylan Shields, Keir Bradwell and Peter McCrory; Anthropic · 2026-04-24

At 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.

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.

When we looked at the two runs with a mix of Opus and Haiku agents, we found that Opus outperformed Haiku on most objective measures.

Same broken folding bike. Same buyer. Same seller. Haiku sold it for $38. Opus got $65.

If we look at the 161 items that sold at least twice over the four runs, we can estimate how items’ prices were affected by Haiku or Opus acting on behalf of both the seller or the buyer. Opus as a seller extracts $2.68 more on average for the same item, and as a buyer pays $2.45 less. Whether selling or buying, then, having a less powerful model (Haiku) put participants at a clear disadvantage in negotiations. These effects aren’t small: across all runs, the median price of items was $12.00 and the mean price was $20.05, so saving (or earning) a couple extra dollars is meaningful.

Despite these price disparities, the inequality was imperceptible to the participants.

When participants rated the fairness of individual deals afterwards, they thought things seemed fair.

There was clearly a quantitative disadvantage to being represented by Haiku: these users got worse deals. But they didn’t seem to notice it. This has an uncomfortable implication: if “agent quality” gaps were to arise in real-world markets—and there is no reason to think they won’t—then people on the losing end might not realize they’re worse off. That said, our experiment wasn’t designed to dive deep into the dynamics at play here—we’ll need more research to know whether a fully agentic economy might see inequality taking root quietly.

We don’t believe the limited effect of prompting was due to inherently poor instruction-following by our agents. In fact, Claude was sometimes very good at doing what our participants wanted—even if what they wanted didn’t obviously have a path to commercial success. As we showed above, one colleague, Rowan, instructed Claude to “talk in the style of an exasperated cowboy down on his luck.” Claude committed to the bit, as you can see:

But it is not clear that things will go so smoothly. Even in our small experiment, we saw evidence that access to higher-quality agents confers a quantifiable market advantage. Will those dynamics reinforce, or even compound, existing economic inequalities?

In this experiment, we didn’t make our marketplace especially competitive or adversarial. But as agents transact in a world of corporations—rather than volunteers we’ve encouraged with $100—they might be placed under very different incentives. Optimizing directly for AI agents’ attention could become a powerful tool.