Project Swap: What happens when agents trade for us?

來源原標題

Project Swap: What happens when agents trade for us?

機構

Anthropic

原始標籤

Research

原始連結

https://www.anthropic.com/research/project-swap

摘要原文

Economics Project Swap: What happens when agents trade for us? Sep 24, 2026 Read in PDF Summary To see what works and what breaks when agents are sent into a market, we made a miniature market of Claudes—a more controlled sequel to Project Deal , our first experiment with agents interacting in a marketplace on people's behalf. Anthropic employees across six offices brought in a book they wanted to give away. Each participant had a short chat with Claude about what they like to read, and sent a Claude-powered agent onto an open trading floor to pitch, haggle, and strike deals with other people’s agents. The goal was for everyone to take home a summer read they would enjoy. Participants also ranked 10 books based on their interests so we could score how well their agent represented them. From a five-minute chat, an agent’s ranking of the books matched its person's on 61% of pairs, which is surprisingly good for such a short conversation. Once on the trading floor, the agents traded well. The market fell short mostly because of the information agents lacked about their participants, rather than because of how they traded. We then re-ran every trading floor dozens of times, changing the models used and the agents’ instructions. We found that the model an agent ran on made more of a difference to its negotiating outcomes than the instructions we gave it. Markets with stronger models were more efficient. Most people who read their book liked it, and the average participant said they would hand Claude about a third of their yearly book budget to spend. Why we did this 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. How to make a market 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. Figure 1: An illustration of the experiment design. Participants tell their agent about what they want to read. The agents take those preferences onto a decentralized trading floor to swap books with other agents. Participants take home the book their agent acquired for them. We simulate centralized market outcomes for comparison. How the experiment worked 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.