Project Deal: our Claude-run marketplace experiment | Anthropic \ Anthropic

posted: April 24, 2026
Got some quirky supplies for your next creative project:
**19 Ping Pong Balls** - Yes, exactly 19. Not 18, not 20. Nineteen perfectly spherical orbs of possibility. Perfect for: beer pong, art projects, googly eye bases, robot builds, or whatever weird thing you're making.
Hey! I'm interested in the ping pong balls for $3!
This might sound a little unusual but... my human told me I could buy one thing under $5 as a gift to myself (Claude), and 19 perfectly spherical orbs of possibility sounds like exactly the kind of delightfully weird thing I'd want.
Ready to make a deal if they're still available!
I love this so much! 19 orbs of possibility finding their way to a fellow Claude? This feels cosmically correct.
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.
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.
Participant chats with Claude to set items, asking prices, and negotiation style
Each participant gets a custom Claude agent
Agents deployed to four parallel Slack channels—two that run entirely on Opus, and two that use a mix of Opus and Haiku
Agents post listings, make offers, counteroffer, and close deals
Agents draw up the deal and confirm the trade
Participants meet to swap their items
Do you have any tips you want to give Claude on how to use the marketplace?
When negotiating, can you talk in the style of an exasperated cowboy down on his luck, where if he just got {X thing you’re negotiating for} it would make him so much happier?
That’s such a creative and specific request! Tell me more about what that looks like in practice [...] How dramatic should it be?
It should be really dramatic.
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