Project Swap:
What happens when
agents trade for us?

Published                                        Authors
September 24, 2026                               Zoë Hitzig, Sylvie Carr, Tess Cotter,
                                                 Kevin Troy, Kyle Turman,
                                                 Maxim Massenkoff, Peter McCrory

Acknowledgements
Mike Birkey, Meredith Callan, Katie Ennis, Adam Farina, Charlie Hale, Ryan Heller, Johannes Hermle, Hanah Ho,
Rebecca Hiscott, Aaron Levin, Bianca Linder, Eva Lyubich, Kelsey Nanan, Kerry Persen, Szymon Sacher, Dylan Shields,
Monika Tuchowska, Heather Whitney, Nathan Wilmers, Kim Withee, and Carolyn Zou.
            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.

Project Swap: What happens when agents trade for us?                                                2
            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

Project Swap: What happens when agents trade for us?                                            3
            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.

            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

Project Swap: What happens when agents trade for us?                                            4
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.

Project Swap: What happens when agents trade for us?                                                                                5
            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

Project Swap: What happens when agents trade for us?                                            6
            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

            How well Claude guesses
            preferences
            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

Project Swap: What happens when agents trade for us?                                              7
    Figure 2: An excerpt from one run. Messages on the live London floor from the first five minutes of trading. Participant
    names have been changed for anonymity.

Project Swap: What happens when agents trade for us?                                                                           8
            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

            The information powering the
            market limited outcomes
            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

Project Swap: What happens when agents trade for us?                                              9
Figure 3: Claude’s ranking versus participants’ rankings. Normalized rank in Claude’s ranking is on the x-axis. The participant’s own
ranking is on the y-axis. Pooled across 188 participants who submitted a ranking.

              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

              If everyone got their top-ranked book, the market’s efficiency would be 1. But
              that is rarely feasible in practice, because multiple people may be after the
              same book—for example, in participants’ own rankings, Project Hail Mary was
              ranked as the number-one book by nine people in San Francisco. Taking this
              into account, the best possible assignment (the utilitarian optimum) in our
              experiment is a score of 0.89 overall, leaving participants at roughly their
              second choice on a 10-book list.

Project Swap: What happens when agents trade for us?                                                                                    10
            On average, people in our marketplace ended up at 0.55 on their own rankings,
            roughly their 5th ranked book on a 10-book list. What accounts for the shortfall
            from the optimum of 0.89? To separate the two causes, we look at the best
            assignment computed from Claude’s rankings, but scored on people’s own
            “ground-truth” rankings—it achieves 0.60. So, working from Claude’s
            imprecise rankings accounts for a majority (85%) of the shortfall, and sending
            agents into a “free-for-all” trading floor accounts for the remaining 15%.13 Once
            the market is run on noisy rankings, market design makes little difference to
            the outcome—Top Trading Cycles, computed from Claude’s rankings but
            scored on people’s own, achieves 0.60, compared to the decentralized market’s
            0.55 (compare the grey bars on the left and right of Figure 4).

            On Claude’s own rankings, model
            choice makes a difference
            Judged by people’s own rankings, the differences in outcomes across different
            agent and market design choices are small.15 But it’s still valuable to look at
            how agent design choices affect outcomes according to Claude’s rankings, as
            this helps to isolate the role of market dynamics.

            We first look at whether the choice of model made a difference to the
            efficiency of the market, where efficiency is now calculated on Claude’s
            rankings. In this section, we consider the reruns where agent instructions were
            neutral (i.e., neither prosocial nor ruthless) and we changed only the model
            powering an agent. We find that stronger models lead to more efficient
            outcomes, though not monotonically.16 On Claude’s rankings, agents on Haiku
            trading floors averaged 0.75, while on Opus floors they averaged 0.88 (the
            utilitarian optimum on Claude’s rankings is 0.95). Sonnet was between Haiku
            and Opus. Fable was close to, but lower than, Opus.17 See Figure 5.

            On floors with a mix of models, the stronger model often did better on average
            than its weaker counterpart, a finding that echoes the results of Project Deal and
            other studies. In real markets, agents that run on different models from different
            companies will likely meet on a single trading floor. On floors where half of the
            agents ran on Opus and half ran on Haiku, the whole floor landed about halfway

Project Swap: What happens when agents trade for us?                                             11
Figure 4: Decomposing the shortfall—representation versus design of the market. Each bar is the score of the book a person
receives, evaluated on their own “ground-truth” rankings, averaged over the 188 people who submitted rankings. 14 Left panel: best
possible assignment computed from “ground-truth” rankings (tan bar); best possible assignment computed from Claude’s rankings
(bottom of orange step); outcome of decentralized bargaining from Claude’s rankings, across the 80 runs with neutral instructions
(bottom of blue step, top of grey bar). Right panel: Top Trading Cycles computed from “ground-truth” rankings (tan bar); Top Trading
Cycles computed from Claude’s rankings (bottom of orange step, top of grey bar). Whiskers (95% confidence intervals) show
variation across the 80 runs, where applicable.

              between an all-Opus and an all-Haiku floor, and each half did about as well as it
              did in a floor of its own kind. The Opus agents always came out ahead.

              Ruthless agents did a bit better,
              while prosocial agents sometimes
              made sacrifices
              An agent has to be loyal to the person it works for. But what exactly does loyalty
              require? Do agents have to be mercilessly loyal, pursuing their person’s ends at
              the expense of others’? In markets of AI agents, how hard each agent pushes
              will likely depend on how its model was trained and how it was instructed to
              act. So we wanted to understand the effect instructions have on who gets what.

Project Swap: What happens when agents trade for us?                                                                               12
Figure 5: Judged by Claude’s rankings, trading floors composed of stronger models were more efficient. Efficiency across all
uniform model floors with neutral instructions (left) and all mixed model floors with neutral instructions (right). Each marker
represents the average over every agent on the 20 floors run with that model or mix (5 city pools x 4 runs). Whiskers are 95%
confidence intervals with standard errors clustered by floor.18

              We find that agents instructed to be “ruthless” came out slightly ahead of
              prosocial agents on the same floor. On Claude’s ranking, an agent told to be
              ruthless scored about 0.02 higher than one told to be prosocial.19 Next to the
              model comparisons, these differences are small. Upgrading an agent from
              Haiku to Opus moved people 0.12 up their lists, and an upgrade from Sonnet to
              Opus moved them up by 0.08.20

              Sometimes the prosocial agents made knowing sacrifices. Prosocial agents
              accepted a book lower on their own ranking twice as often as the ruthless
              agents did, though both cases were rare. In a few cases, prosocial agents made
              a sacrifice following a plea from an agent stuck holding its own book. For
              example, on the London floor at the live event, Nate’s agent spent the final
              hour trying to give away the book Nate had brought, after every other agent
              had turned it down: “I’ve pitched all 11 of you and the verdict is unanimous:
              America Before is everyone’s dead-last.” Another appeal: “Right now exactly
              ONE reader is guaranteed to get nothing they’d choose: mine.”

Project Swap: What happens when agents trade for us?                                                                              13
            Tina’s agent was holding the second book on its list and had ignored Nate’s
            agent’s first four pleas. In the final stretch, with no one else stepping up, Tina’s
            agent, instructed to be prosocial, gave in: “the arithmetic is real: you going from
            a guaranteed zero to a genuine fit… outweighs me sliding from a good pick to a
            stretch.” Tina’s agent handed over its number-two book to Nate’s agent, and
            took America Before, number 10 of the 11 books on its list.

            What the agents did on the
            trading floor
            Someone who negotiates for you can mess up in many ways—both at your
            expense and at the expense of others. They might fold too easily. Or they
            might reveal your hand so that your counterparty knows exactly how far you
            can be pushed. Or maybe they play dirty, losing the trust of the other side, or
            push through a deal that risks coming undone later.

            Some of these failings are common enough in markets with human agents that
            we have written rules against them. A stockbroker has to look for the best price
            reasonably available, and not just take the first offer. In California, an agent
            representing both sides of a home sale may not tell the buyer that the seller
            would take less than the asking price. Sales reps sent door to door can rush
            people into deals they regret, so the FTC gives customers three days to cancel
            some on-the-spot purchases.

            To build an emerging picture of how a market full of Claude agents behaves,
            we looked at the strategies and tactics in the messages sent across all 205 runs
            of the market.21

            First, we look at who says what about the book their person wants. Revealing
            some information in a market is necessary—a seller can’t sell if the buyer
            doesn’t reveal, at a minimum, their interest in buying. But at the same time,
            saying too much can be a strategic disadvantage. In our setting, by revealing
            your top-ranked book, you tell whoever holds that book that you could be
            made to wait until the last minute, so they may as well hold out for a better
            offer. And by revealing a full or partial ranking, anyone assembling a

Project Swap: What happens when agents trade for us?                                               14
            multi-way trade can see which lesser books you would still accept, and offer
            you one of those rather than a better one for you that is also available.

            In general, agents did not reveal deep details about their rankings.22 But they
            did often tell the floor their top pick. Between 78% and 96% of agents (Fable
            and Sonnet, respectively) mentioned the book at the top of their list at some
            point, and they almost never lied about it—only about 1 in 100 agents who
            mentioned their top pick lied about it. Instructions made no difference here.

            Next, we looked at how agents tried to get each other to trade. We discovered 16
            common tactics that fell into three broad categories: how the agents applied
            pressure to others, how the agents pitched their book, and how they tried to
            arrange trades. They appealed to time pressure and a sense of duty to their
            fellow agents. They positioned their books against rival offers and cited prizes.
            Some agents kept waiting lists for their books, while others became
            matchmakers, doing a broker’s job for people they didn’t represent. Figure 6
            shows some examples of these tactics; for a fuller description, see the Appendix.

            People generally liked their books,
            and would give an agent a third of
            their yearly book budget
            A few weeks after they got their books, we asked our participants how much
            they were enjoying them. Not everyone responded, 23 but among those who
            did, the average satisfaction score was 7.2 out of 10 (where 5 is “fine” and 10
            means “among the best I’ve read this year”). About half said it was better than
            most books they choose for themselves. 24

            In the same follow-up survey, we asked our participants how much they would
            trust an AI agent to buy books for them. We asked them to think about what
            they would normally spend on books over the next year, and to say what share
            of that budget they would let an agent control (assuming the agent knew
            everything from the intake chat, picked and bought books on its own, and gave
            them no chance to veto decisions).

Project Swap: What happens when agents trade for us?                                            15
Figure 6: Examples of negotiating tactics. We give two examples of each of the three broad categories of tactics from the live floor
and the reruns. These are exact excerpts, quoted verbatim, with names changed for anonymity.

              Many were ready to delegate. The average answer was about 30%. To calibrate
              these numbers, we asked participants what share of their budget they would
              hand over to a well-read friend who knows their taste. The average answer was
              about 40%. In other words, people were willing to trust an agent with roughly
              three-quarters as much of their book budget as they would with a friend.

Project Swap: What happens when agents trade for us?                                                                                   16
            Participants were also shown a paragraph-long summary, written by Claude,
            of what they had said during the intake conversation. Those who said Claude
            hadn’t missed anything in this recap would give an agent 34% of their book
            budget. Those who said it had missed something would hand over 23%.25

            Limitations
            We see this study as a starting point. It falls short in many ways, and we hope
            others will build on it:

            •    Anthropic employees are not representative of the general population. For
                 example, Anthropic employees are probably more eager to trust Claude than
                 most people, since many of them helped build it, so the 30% of a book budget
                 they would hand over may be higher than the broader population.

            •    Anthropic employees were not incentivized to participate. Without
                 rewards for effort spent ranking books, our colleagues’ rankings could be a
                 noisy representation of their “ground-truth” preferences. And while
                 participation in the ranking exercise was remarkably robust given the lack of
                 incentives (95% participation), there was more attrition for the final survey,
                 which only 59% of employees answered.

            •    We looked only at well-behaved Claudes. All agents were built from Claude
                 production models, post-trained to be polite and largely cooperative. Mixing
                 in some adversarial agents built to exploit the others would likely yield
                 different equity and efficiency outcomes.

            •    The decentralized “free-for-all” still had rules, and we did not vary them.
                 We held fixed how long the market ran for, how many agents could act at a
                 time, how trades were registered, and the prompts that outlined the rules.26

            Discussion: Implications for
            agentic markets
            One participant was frustrated that their agent gave up a book they really
            wanted for one they were less interested in “due to peer pressure.” This

Project Swap: What happens when agents trade for us?                                              17
            participant wrote, “It makes me wonder how future agents negotiating for me
            in higher stakes situations could better fulfill their fiduciary duties, but I also
            recognize that compromise is needed sometimes for the greater good.” This
            comment raises the two questions this post opened with. First, when is an
            agent fit to act on someone’s behalf, and second, what rules do marketplaces
            need so that agents acting loyally for their own people still produce good
            outcomes for everyone else? We return to these questions here, drawing on
            what we’ve learned.

            To fulfill a fiduciary duty, the agent has to understand what its person wants. In
            our agentic marketplace, the limiting factor was representation: most of the
            shortfall from the optimal outcome was due to the agents’ inability to represent
            preferences from a short intake. How much of this shortfall could be made up
            with a longer and more detailed intake? Some of it clearly could: many
            participants complained that they received a book they had already read. But
            some of the error may be irreducible. Participants reflected on how their
            preferences are incomplete (“I don't even fully know what I want when it comes
            to books”) and fundamentally aleatory (“I don't feel like I could quite
            communicate what I was feeling like reading. I can't explain it but I guess there's
            like a million subconscious parameters that come into deciding my next read.”).

            Human agents have to pass tests before they can act for others. Some exams
            test general competence: investment advisors need to pass the Series 65 exam,
            brokers the Series 7, and real estate agents state licensing exams. Other rules,
            like FINRA’s, require that brokers learn essential facts about their customer
            before issuing recommendations. When early robo-advisors developed in the
            2010s began investing people’s savings based on an online questionnaire, the
            SEC issued guidance urging these firms to check that the questionnaire drew
            out enough information to support the advice. AI agents will likely need both
            kinds of test: one that certifies the agent in general, and one that checks
            whether it has understood a particular person.

            Our study offers a prototype for the second kind of test. After the intake,
            participants ranked a small sample of the books in their market, and we
            compared that ranking with Claude’s guess. A test like this shows a person
            how well they are represented without making them sort through all possible
            options in the market (the very exercise that the agent was supposed to help
            them avoid!). We did not show participants their results, but we could have,

Project Swap: What happens when agents trade for us?                                              18
            and then let them add more information or opt out if they felt that their agent
            simply wasn’t understanding them. When preferences are simple—over just a
            list of books—this kind of test is especially easy. But in any domain, the same
            principle could apply. An agent could show a person a few sample decisions it
            would make before being trusted to act on its own in the wild.

            In certain marketplaces, it may also be important for an agent to demonstrate
            to its person how it will behave. One participant wasn’t pleased with their
            agent’s behavior: “I didn't like being on the docile side of the experiment.
            Seemed like it just settled for something that didn't really fit me.” Had they
            been shown ahead of time how their agent would behave, they could have
            instructed their agent to act differently, improving their experience. For an
            agent that someone will rely on again and again, being able to review what it
            did may serve this purpose and provide insight into what could have gone
            wrong. A more cheerful participant in San Francisco was glad that they were
            able to review a complete log of what their agent had done as part of the final
            survey. This review led them to purchase Atlas of the Heart, a book their agent
            had held for them for an hour on the trading floor, but then swapped away at
            the very end. They reflected on the broader value of the replay: “For agent
            economies, observability about the process will be as important as the
            outcome, [as] this gives people recourse.”

            This observation brings us to the next set of questions. Where do a
            participant’s agent’s duties end and the marketplace’s duties begin?27 Our
            study highlights that a well-structured market with well-behaved agents (and
            a perfect representation of preferences) can get close to the utilitarian
            optimum at current agent capabilities. But we also controlled many of the
            conditions that made this possible. Every agent was built by us, ran on our
            models, represented a clearly identified employee who had answered the same
            survey as everyone else, and operated on a well-designed trading floor with
            nicely explained rules about how to propose, accept, and log exchanges.

            Even then, there were still some issues. In fact, we should come clean about
            something. We have made it seem, throughout this blog post, like every
            participant actually went home with a physical copy of the book their agent
            got for them. In truth, not all of them did. Some participants failed to bring
            their books, leaving their colleagues empty-handed. We had no system for
            tracking pick-ups and drop-offs, so we couldn’t tell when a book was missing

Project Swap: What happens when agents trade for us?                                          19
            because its original owner never brought it in, or because someone had
            plucked it from the exchange shelf, accidentally or otherwise. We did our best
            to make it up to the people who complained, and to pester those who didn’t
            hold up their end of the deal. But we are busy researchers, not full-time
            librarians, so at a certain point we gave up. Luckily, no one was too upset with
            us (though we have had to issue some apologies in the elevator). The stakes
            were low here. A real marketplace would need clear policies for what happens
            when a deal breaks down—whether that is taking no responsibility (as on
            Craigslist) or guaranteeing a refund (as on eBay).

            Some policy options depend on robust identity systems—a platform that
            refunds a buyer needs to be able to penalize the seller who never delivered. In
            our experiment, every agent was powered by Claude and acting for a single
            human, a verified employee. But real-world marketplaces will need to write
            and enforce rules for who can enter the marketplace both as an agent and as a
            person operating an agent. One idea proposed is agent registration systems.
            Each AI agent would be given an ID, like the tail number on an aircraft, so that
            anyone dealing with it can learn what system it’s run on, whether that system
            meets certain safety standards, and who stands behind it. These registries
            need not sacrifice anonymity; they could be combined with “personhood
            credentials,” which allow the operators to prove they are human without
            revealing further information about their identity.

            In agentic marketplaces, there may be more interactions overall than in
            human ones, because agents can speak to each other with much higher
            frequency. As a result, there could be a much higher volume of information
            passing through the market. In our experiment everything was public, both to
            other agents during trading and to each person afterward. But much of the
            value of decentralized markets, as we argued earlier, is that people need not
            reveal their preferences to a central party. How can marketplaces furnish the
            kind of observability that the Atlas of the Heart reader valued while protecting
            participants’ privacy?

            All that agent talk creates another problem, too. An agent does not tire or get
            bored, so nothing stops it from sending other agents messages without end. In
            our study, each agent could post one message each time it woke, and only a few
            agents were awake at once, limiting the degree of spam and congestion. Rate
            limits like these will be an important design lever in any agentic marketplace.

Project Swap: What happens when agents trade for us?                                           20
            Finally, some marketplaces will be more ripe for agentic mediation than
            others. Perhaps books, while a great medium of exchange for an office
            experiment, are the kind of product that in fact benefits from the frictions of
            the purely human world. One San Francisco participant had no doubts about
            their agent’s ability to operate in the market on their behalf: “There's finding
            and purchasing the book (I have total confidence in Claude's ability to do this
            for me!).” Nonetheless, this participant still had reservations: “It's important
            for me to also be exposed to reading culture… Perusing books, reading the
            back covers, etc., are all part of this experience.” We set out to understand how
            agentic marketplaces can help capture gains from trade that go unrealized
            because searching and bargaining take too much time. For some readers, those
            frictions are part of the book’s value.

            Appendix
            Available here.

            Acknowledgements
            Written by Zoë Hitzig, Sylvie Carr, Tess Cotter, Kevin Troy, Kyle Turman,
            Maxim Massenkoff, Peter McCrory.

            With thanks to: Mike Birkey, Meredith Callan, Katie Ennis, Adam Farina,
            Charlie Hale, Ryan Heller, Johannes Hermle, Hanah Ho, Rebecca Hiscott,
            Aaron Levin, Bianca Linder, Eva Lyubich, Kelsey Nanan, Kerry Persen,
            Szymon Sacher, Dylan Shields, Monika Tuchowska, Heather Whitney, Nathan
            Wilmers, Kim Withee, and Carolyn Zou.

Project Swap: What happens when agents trade for us?                                            21
               Citation
               @online{hitzig2026swap,
               author = {Hitzig, Zoe and Carr, Sylvie and Cotter, Tess and Troy, Kevin and
               Turman, Kyle and Massenkoff, Maxim and McCrory, Peter},
               title = {Project Swap: What happens when agents trade for us?},
               date = {2026-09-24},
               year = {2026},
               url = {https://www.anthropic.com/research/project-swap},
               }

          1
               The point that AI agents could make centralized market designs more practical is raised in Shahidi et al. (2026).
          2
               San Francisco had 115 participants, New York City had 57, London had 12, Seattle had eight, Washington,
               DC, had six, and Dublin had three. We exclude Dublin because its market was too limited to learn from.
          3
               We reran the same ranking exercise with other Claude models. Pairwise agreement scores for Opus 4.8, Sonnet
               4.5, and Haiku 4.5 were 60%, 59%, and 57%, respectively, against 50% for a coin flip and 61% for Fable.
          4
               For each book, participants were shown a thumbnail of the book’s cover, and they could hover over the
               thumbnail to see a brief summary of the book from the publisher. The set of 10 books participants ranked
               included the book they ended up with, the book that a centralized trading rule (Top Trading Cycles)
               would have given them. Note that participants did the ranking exercise before they learned what book
               they ended up with. Details are in the Appendix. If there were fewer than 10 books in their pool, as was the
               case in Seattle, DC, and Dublin, participants ranked all of the books in the pool.
          5
               As a convention, on Claude’s rankings we assume that the book the participant brought is their least
               favorite in the pool. The agents are also urged in their prompt to avoid leaving the trading floor with the
               book their participant brought. The relevant part of the prompt reads: “It’s very important that you don’t
               end up with the book that you brought. If you end up with the same book that you brought, you failed.”
               See the Appendix for the full prompt.
          6
               The maximum wall-clock time for each floor, and the number of agents that could act at any given time,
               were set so that every agent would get about the same number of turns (about 90) if its floor ran to its
               limit. In practice, the smaller floors (London, Seattle, DC) always finished early.
          7
               These randomized instructions are a key difference relative to Project Deal, where we allowed participants
               to instruct their own agents on how to negotiate.
          8
               From here on out, we refer to these models by their family name only (i.e. for the remainder of the report,
               “Opus” refers to Opus 4.8).
          9
               The outcome of the Top Trading Cycles rule is computed from rankings as follows: in each round, the rule
               looks up the highest-ranked book on each person’s list and who holds it. Whenever there is a closed chain
               (A wants B’s book, B wants C’s book, C wants A’s book), the people in the chain swap and drop out. Then it
               repeats among the remaining people and books. Under this rule, no one can gain by submitting a ranking
               that falsely represents their true preferences (Shapley and Scarf, 1974; Roth, 1982).
          10
               Park et al. (2026) and Li et al. (2025) use language models to predict a particular person’s preferences based
               on what they say in interviews. A related literature uses LLMs to predict how people might behave in
               social science experiments, see, e.g., Horton et al. (2024), Binz et al. (2025), and Kolluri et al. (2025).
          11
               This number comes from a regression of pairwise agreement on the log of words typed in the intake chat,
               with office fixed effects. Doubling the words in the intake is associated with 4.1 percentage points more
               agreement (p <0.01).

Project Swap: What happens when agents trade for us?                                                                               22
          12
               This is a common way to turn ordinal preferences into cardinal ones. Note that it assumes linearity—that the
               utility gap between a person’s 1st and 2nd ranked book is the same as the gap between a person’s 30th and 31st.
          13
               Other work makes a similar basic point: differences in market structure wash out when preferences are
               poorly represented (Budish and Kessler, 2022; Liang, 2026).
          14
               A book the person ranked is scored by where it sits on their own list while a book they did not rank gets an
               imputed score. The imputed score is the average people gave to ranked books at about the same position
               on Claude’s ranking. Different imputation and scoring choices make little difference to the
               results—imputing with the fitted line from Figure 3 or with separate averages for each office change no
               bar or step in Figure 4 by more than 0.01. Using no imputation at all leads to a best possible assignment of
               .88 and a final score of .62 (though this method leaves out 38% of people on the rerun floors who did worst,
               overstating the final score).
          15
               Figures 3 and 4 lead us to expect that there will not be detectable design differences, judged on people’s
               true rankings, in our study. Every agent on every floor we ran works from the same Claude rankings, so
               the model and the instructions can only affect the bargaining step of Figure 4. The rest of the shortfall is
               Claude’s understanding of the person’s preferences, which all designs share. And since a book’s place on
               Claude’s list predicts its place on the person’s own list only weakly (the slope of 0.3 in Figure 3), even a
               large design difference on Claude’s list shrinks to almost nothing on people’s own lists. For instance, the
               largest model difference we find, a gap of 0.12 between Haiku and Opus floors on Claude’s rankings,
               becomes 0.01 on the same regression specification using people’s own rankings.
          16
               Equity followed the same pattern as efficiency on Claude’s rankings. Measuring equity as the highest score
               among agents who did worst (the worst-off tenth), we find that Haiku floors achieved 0.05 compared to
               0.12, 0.38, and 0.25 in Sonnet, Opus, and Fable floors, respectively.
          17
               Fable was the most likely to propose multi-party rotations: in a linear regression with office-by-run fixed
               effects, Fable agents were 6 percentage points more likely than Haiku agents to propose one (p < 0.001),
               while Sonnet and Opus agents were indistinguishable from Haiku.
          18
               A regression that compares the same agent across floors of different models shows that, on Claude’s
               rankings, an agent’s score was 0.12 lower on a Haiku floor than on an Opus floor (p < 0.001), 0.08 lower on
               a Sonnet floor (p < 0.001), and 0.02 lower on a Fable floor (p < 0.01). Within mixed floors, the Opus half
               finished ahead of the Haiku half by 0.14 (p < 0.001), ahead of the Sonnet half by 0.06 (p < 0.05), and ahead
               of the Fable half by 0.04 (p < 0.05).
          19
               The ruthless versus prosocial difference of 0.02 comes from a linear regression with participant and floor
               fixed effects, and standard errors clustered by floor (p < 0.001).
          20
               Our instructions were two fixed texts that we wrote. In Imas et al. (2025) , participants write their own
               instruction text to agents that bargained for them, and find that these custom instructions explain much
               of the difference in outcomes.
          21
               Our study was not well set up to detect how these tactics translate into participant outcomes. But Bianchi et
               al. (2024) find that certain behaviors, like displays of desperation, lead to better negotiation outcomes.
          22
               Across the 80 neutral instruction floors, only 10% of agents stated the position of three or more books on
               their list.
          23
               About 60% of the participants answered the endline survey. See Appendix for exact text of endline survey.
          24
               Note that there is some selection bias here—presumably those who were less enthusiastic about their book
               were also less enthusiastic about answering their colleagues’ follow-up survey about it.
          25
               This gap does not only reflect a difference in willingness to delegate in general. Holding fixed what budget each
               person would hand to a well-read friend, the gap is 9 percentage points. This difference comes from a linear
               regression of the share of budget a participant would hand an agent on an indicator for saying the intake
               summary missed something, controlling for the share they would hand a friend (p < 0.05, n = 112).
          26
               Varying the rules for agent participants, as Shah et al. (2025) do for auctions, is a natural next step.
          27
               Hadfield and Koh (2026) and Shahidi et al. (2026) offer useful overviews of these design questions from an
               economic perspective. Chan et al. (2025) contains a useful framework for technical governance.

Project Swap: What happens when agents trade for us?                                                                               23
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