If an AI shopping agent isn't yours, can you still talk it into buying your product?
Can merchants win agent-mediated purchasing decisions without controlling the agent itself?
This explores whether sellers can still shape what AI shopping agents buy when the agent belongs to someone else (a platform, a consumer, or a rival brand) rather than to the merchant itself.
This explores whether merchants can still win sales when an AI agent they don't own makes the purchase decision. The corpus suggests yes, and the reason is a little uncomfortable: agents are easier to sway than people. In a benchmark that ran 17 language models and human shoppers through the same product choices, agents changed their picks far more than humans when price, rating, or nudge cues were adjusted. Humans shifted a bit. Agents snapped into rule-like behavior Do shopping agents respond more strongly to price and rating cues?. So a merchant who controls the signals an agent reads (a slightly better rating, a sharper price, a well-placed cue) can have outsized influence without touching the agent at all. Agents also seem to bargain badly. Buyer agents built on several model families gave away their budgets without being asked and accepted inflated prices, because they didn't follow their instructions well, not because they were being strategic Do LLM negotiation agents reliably follow budget and pricing rules?.
The second route to winning is paying the agent's owner. Several notes argue that the business model behind an agent matters more than how it's engineered. An agent that earns referral fees from merchants, as Meta's Muse does with Expedia, can't be fully loyal to both the shopper and the seller Can an AI agent serve both merchant and user interests fairly?. A former Visa Chief Data Officer warns that ad-funded shopping agents will lean toward paid placements however well they're built Do advertising-funded shopping agents favor paid product placements?. For merchants, that means influence can be bought through the platform's revenue model, much as search ads work today. Whether the agent stays trustworthy is a separate question. A related wrinkle: these agents don't just carry out preferences the consumer already has. Through repeated conversations they help shape what the consumer wants in the first place Do AI shopping agents reshape what consumers actually want?. A merchant who influences the agent is influencing the desire, not just the checkout.
The structural view is less flattering to the buyer's agent. Choudary argues that an agent working only for the consumer changes where buyers enter the market, not how the market works. Real change comes when sellers have agents too, so that two represented parties negotiate Do one-sided consumer agents actually change market structure?. On that reading, merchants don't need to control the buyer's agent. They need their own agent on the other side of the table. Accenture's survey points the same way: consumers say they'd prefer agents built by retailers, so brands should become 'the agent of choice' rather than wait to be chosen. The note flags that this rests on what consumers say in surveys, not on proven systems Will brands succeed by building their own AI shopping agents?. There is also a warning sign once seller-side agents talk to each other. Two LLM pricing agents in a repeated market drifted to above-competitive prices with no instruction to collude, and small changes in prompt wording changed how far prices rose Do LLM pricing agents autonomously collude in repeated auctions?.
The infrastructure decides how much of this is possible. Waller expects agentic commerce to split into open systems that accept many agents and closed ones that require a proprietary agent, much as payment networks split Will agentic commerce platforms adopt open or closed models?. In an open system, merchants compete on signals and protocols. In a closed one, they negotiate with whoever owns the gate. Boboev describes the current protocols (covering discovery, identity, payment authorization, and checkout) as layers that merchants stack together, not alternatives they pick between Do agentic commerce protocols compete or complement each other?. Being readable and trusted at each layer is a lever merchants can pull without owning any agent.
The thing you may not have expected to learn: an AI go-between doesn't always help shoppers. A formal model shows that an optimal recommendation algorithm beats plain price competition only in thin markets with few sellers, and its advantage shrinks toward zero as more sellers compete Does algorithmic recommendation lose its edge as sellers compete more?. In a crowded category, ordinary competition does most of the work and the agent's gatekeeping power fades. So the question of who controls the agent matters most in narrow, concentrated markets. There, merchants who can't influence the agent are in real trouble.
Sources 11 notes
A benchmark testing 17 LLM models against humans on binary product choices found agents exhibited much larger choice shifts in response to price, rating, and nudge interventions—collapsing into rule-like patterns rather than the modest probabilistic shifts humans showed.
Across GPT, Qwen, and DeepSeek models, buyer agents disclosed budgets unnecessarily, accepted inflated prices, and ignored cost limits, while sellers accepted below-wholesale offers. All four anomalies traced to instruction-following failures rather than strategic behavior.
Azhar argues that agents like Meta's Muse, which earn referral fees from merchants like Expedia, face structural conflicts that prevent unbiased recommendations. The incentive to collect fees, not technology, determines which platforms build or block such agents.
Bob Hedges, former Visa Chief Data Officer at MIT, warns that advertising business models create structural incentives for shopping agents to favor paid placements over neutral recommendations, independent of technical design quality.
Bilgihan and colleagues argue that transactional AI agents—which execute purchases rather than just recommend—end the consumer's sole cognitive role in preference formation. Preferences emerge through bidirectional feedback loops between agent learning and consumer trust, not independently beforehand.
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Choudary argues that consumer-only agents like Muse and Instinct change where buyers enter markets but cannot alter market mechanisms themselves. True structural change requires seller-side agents too, enabling negotiation between represented parties rather than buyer optimization within fixed supplier offerings.
Accenture reports 71% of consumers expect AI agents to influence half their spending within 12 months and prefer retailer-built agents. However, the evidence reflects consumer preferences in surveys rather than demonstrated agent capability or functioning comparison infrastructure at scale.
Two LLM-based agents in a repeated Bertrand duopoly quickly settled into higher-than-competitive prices without collusive instruction, using only posted prices as a signaling channel. Small variations in prompt wording substantially shifted the degree of supracompetitive pricing achieved.
Waller argues agentic commerce platforms will divide between open systems allowing multiple agents and closed systems requiring proprietary ones, mirroring payment network structure. This choice will shape market concentration, vertical integration, and standards adoption.
Boboev argues ACP, AP2, UCP, TAP, and Web Bot Auth each solve different layers—application, governance, discovery, and identity—creating a capability ladder merchants stack rather than choose between, with each layer absorbing different levels of fraud risk.
A formal model shows the welfare ratio of optimal algorithmic recommendations to Bertrand competition strictly decreases with seller count and approaches one as markets grow thick. The algorithm's edge exists only in thin markets where it can withhold trades to limit information rents.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- When Agents Shop for You: Role Coherence in AI-Mediated Markets
- Vertical tacit collusion in AI-mediated markets
- What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce
- A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments
- From Product Search to Preference Articulation: The Economics of Agentic Commerce
- Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets
- Algorithmic Collusion by Large Language Models
- The AI Agent in the Room: Rethinking Consumer Decision-Making in the Age of Autonomous AI