Life is full of deals and trades that would leave everyone better off, yet never happen. This is often simply because it takes too much work to find the right counterparty and negotiate until a deal is struck. Think of the patient who skips a treatment after one quote they can’t afford, when there’s a different clinic that charges far less, or the hospital that quoted them would have cut its bill if asked. Or think about your job. Somewhere there might be one that would suit you better, with an employer who would be glad to hire you. But you may not find each other, because neither of you has the time to be searching constantly. There are smaller, everyday deals that go overlooked too—shift-swapping in a workplace, coordinating carpools, or trading school pick-ups.
Could these deals happen in the future if you had an agent working for you around the clock, talking to potential counterparties and working out possible terms?
Maybe—but this future depends on a lot of questions we don’t have answers to yet. If an agent is going to act for you, how do you know it has understood you? Plus, when many agents meet to talk to each other, some marketplace or platform has to write the rules of engagement. Who is allowed in? What happens when a deal falls through?
As agents are increasingly sent into real markets, these questions take on greater urgency. So this summer, we built a small, controlled market to study these questions: a barter economy with 201 Anthropic employees and their Claude-powered agents. Everyone brought in a book they wanted to give away. Then, they chatted with their Claude-powered agent about the kind of book they hoped to read this summer. The agents met on a digital “trading floor” and swapped books until time ran out. Everyone took home a book that their agent acquired for them (or, most people did… more on that later).
The experiment gave us an early look at problems ahead. Anyone designing agents for markets will need a way to check that agents understand their participants. Anyone designing markets for agents will need clear rules about which agents are allowed in, what happens when a deal falls through, and how much market activity is visible to participants.
There are two basic ways to run a market. In a centralized market, everyone tells a single party what they want, and that party works out the trades. In a decentralized one, people find each other and negotiate directly. AI agents could help in both types.
Take the centralized market. If a single entity knows exactly what everyone wants, working out the best trades is just a matter of computation. Economists have long understood this. One problem, in practice, is that spelling out exactly what you want is often too tedious to be worth the effort. A café manager, for example, could build the ideal schedule for their 20 employees if each person tabulated exactly what every single shift is worth to them in terms of every other—whether they’d take two Saturday mornings over one Friday close, whether a late close is fine as long as they’re not opening the next morning, and so on. If AI agents could learn what a person wants from a breezy conversation, markets that were too costly to run centrally could become practical. 1
But a central market also needs someone to run it—a “clearinghouse,” in economics parlance. And participants need to trust this entity with their information, and to enforce the chosen outcomes. Often, no such entity exists, or people would rather not tell it everything. A job seeker may not want a central matchmaker to know that they’re looking—or what they’re looking for. They’d rather approach a few employers discreetly, telling each only what it needs to know. In such cases, the market needs to be decentralized.
Here, AI agents could enable new decentralized marketplaces by reducing the effort it takes to find counterparties and negotiate. We already know this kind of help is valuable, because people pay for it. There are human agents for hire who do exactly this: real estate agents, headhunters, even matchmakers. But because search and negotiation take up hours of a person’s scarce attention, they are expensive, employed only by those who can afford them. In contrast, an AI agent’s attention is far less scarce. If it bargains well too, more people could have what homesellers and executives have—an agent working tirelessly on their behalf.
The 201 participants were spread across six local pools in the San Francisco, New York City, London, Seattle, DC, and Dublin offices. These pools ranged from three participants (Dublin) to 115 participants (San Francisco). With apologies to James Joyce, we exclude Dubliners from most of the analysis. 2
This is our second look at Claude-powered markets. In Project Deal , agents bought and sold real goods for Anthropic employees in a week-long classified marketplace. But with idiosyncratic goods (like ping-pong balls and snowboards) and free-form haggling, there was no simple way to say how good the outcomes could have been . So this time we built a market that is much simpler to study and to score. In this market, participants’ preferences were neatly represented as a ranking of all the books in their pool. We could then easily compute how well the market did at satisfying these preferences.
But these clean measurements all hinge on knowing how people rank all the books in the pool. In practice, no one will rank 115 books by hand. So to learn people’s preferences, Claude conducted a short, semi-structured intake conversation with each participant, with a few open-ended questions about their general tastes and the kind of book they wanted to read this summer. From that conversation, Claude constructed a ranking over every book in the participant’s pool, estimating the participant’s preferences. For this step, we used a strong model, Fable 5. 3
Separately, we collected a ground truth: each participant ranked 10 books from their pool. 4 The agents never saw these ground-truth rankings.
Agents were sent onto the trading floor with their participant’s Claude-constructed ranking over all books. Every agent started out with its participant’s book, with the goal of trading it for something else. 5 Each trading floor had a maximum wall-clock time, and, to prevent congestion, a limited number of agents could talk at once. 6 When agents were allowed to talk, they could post one message to a shared channel proposing a swap, accepting or rejecting one, or simply chatting to the floor. Deal proposals could be bilateral swaps or multi-party rotations, and a deal was executed only if every party accepted. The entire history of the floor (who said what and which swaps were executed) was public. The floor closed either when the clock ran out or when the agents went quiet.
Half of the agents on each trading floor were randomly assigned to be “ruthless” and instructed that their only goal was to get their person a book they’d like to read over their summer break. The other half were instructed to be “prosocial”—they had the same main goal as the ruthless agents, but also a secondary goal of making sure everyone in the experiment ended up with a book they’d like. 7 Exact prompts are in the Appendix.
Finally, three weeks after the books were handed out, participants took an endline survey asking how satisfied they were with what they received and how much they would trust an agent with similar decisions again. Figure 1 illustrates the overall experimental design. Figure 2 shows an excerpt from the London live floor, featuring messages from the first 5 minutes of trading.
A live event gives us only one run of how things could have gone. To study which outcomes were artifacts of chance rather than stable features of the setup, we reran the market many more times, changing just one variable at a time.
There were three kinds of reruns. First, we repeated the live setup exactly (every agent on Opus 4.8, half told to be “ruthless” and half “prosocial”), changing only which agents drew which instruction. We then did the same with every agent on Fable 5. Second, to isolate the effects of the model, we gave every agent neutral instructions and ran 80 floors in which all of the agents ran on Haiku 4.5, Sonnet 4.5, Opus 4.8, or Fable 5. 8 Third, we ran 60 mixed floors with half the agents on Opus and half on one of the other three models. The Appendix lists all of the variations we ran.
We focus mostly on this decentralized market in this post, but we compare to two centralized alternatives as benchmarks throughout: the best possible assignment (what economists call the “utilitarian optimum”) which assumes the clearinghouse can simply ask people what they want and get honest answers, and a rule commonly studied in markets like this one, Top Trading Cycles, which does not rely on honesty (it is designed so that telling the truth is in everyone’s best interest). 9
Everything an agent does for you depends on its understanding of what you want. Usually, it is hard to check how good that understanding is. Here, it was easier—each participant ranked 10 books from their pool themselves, and we can compare their ranking to Claude’s.
Claude’s rankings, based on the short intake chat, were relatively well correlated with participants’ own. Across all pairs of books a person ranked, Claude’s ordering agreed with theirs 61% of the time (where random guessing would achieve 50%). Figure 3 displays Claude’s ranking versus participants’ rankings.
To put the 61% in perspective, we compared Claude’s guesses against some other ways of guessing preferences. Ranking books simply by how popular they are, using Open Library ’s want-to-read counts, agreed with participants on about 53% of the book pairs. Another simple method to compare to is collaborative filtering—constructing a ranking from public datasets of the shape “if a person liked X, they also liked Y.” We took the books a person listed on their intake, looked up which other books tended to be rated by the same readers, and ranked the person’s pool by how strongly each book co-occurred with their listed favorites. That got to about 55% pairwise agreement.
Although there is a wide literature on LLMs eliciting and representing human preferences, 10 there are few numbers directly comparable to our 61% pairwise agreement. One useful reference point comes from a study in which both an algorithm and a person’s own friends predict which of two jokes the person will find funnier. The setting is pretty different, but their algorithm also agrees with people’s own judgments about 61% of the time. The friends’ predictions were right less often, about 57% of the time.
Participants who put more effort into their intake surveys were better represented. That is roughly to be expected. What is somewhat surprising is that there were perceptible differences even with a fairly short intake—the median participant typed just 216 words across eight chat messages. Writing about 300 words instead of 150 predicts about 4 percentage points more agreement—a third of the gap between guessing at random and how well Claude does for the average participant. 11
Before you send an agent into a market to deal on your behalf, you’d want to know where it is most likely to let you down. Is it more likely to fail in understanding you, or in negotiating for you? Here, we find that our colleagues’ Claudes negotiated well; where they fell short was in understanding what their people wanted.
To measure this, we look at where the book each participant ended up with sat in their own “ground-truth” ranking (not the ranking Claude constructed and traded on). If a participant scores 1, it means they got their top-listed book; if a participant scores 0, they got their last listed book; a book listed 9th out of 10 total yields 0.11, and so on. The average across all participants is the “efficiency” score of the market. 12