Picture this: You wake up on a Tuesday morning, check your email, and find three order confirmation receipts for items you have no memory of buying. Your AI shopping agent — the one you set up last month and promptly forgot about — has been busy overnight. It found a sale, matched your previous preferences, decided the price was within your loosely defined budget parameters, and completed three purchases across two different retailers while you slept. Everything it did was technically within the permissions you granted. None of it is what you actually wanted.
Welcome to agentic commerce. Welcome, also, to one of the most legally and privacy-uncharted territories in the modern digital economy.
Agentic commerce is a new model of online shopping where AI agents do not just recommend products but actually complete purchases on behalf of users — handling the entire transaction workflow: finding products, comparing options, checking availability, applying discounts, processing payment, and confirming orders. The human user sets parameters and approves decisions rather than clicking through each step manually. The technology is not hypothetical. OpenAI has embedded native checkout features into ChatGPT. Perplexity offers purchase functionality for paid subscribers. Amazon’s “Buy For Me” feature routes payment and shipping details to third-party merchants on a user’s behalf. Mastercard has introduced agentic tokens specifically designed to enable AI-initiated payments at scale.
Morgan Stanley predicts that nearly half of online shoppers will use AI shopping agents by 2030, accounting for approximately 25% of their spending. The infrastructure is already live. What is conspicuously absent is the legal and privacy governance framework to match it. And for privacy professionals and compliance teams, that absence is not someone else’s problem to solve.
The Third Actor Nobody Planned For
Every consumer protection framework ever written assumes a two-party transaction: a buyer who makes a decision and a seller who fulfills it. Liability follows intent, and intent is attributed to the human who clicked, signed, or swiped. Agentic commerce inserts a third party into that transaction — an autonomous system that is simultaneously an extension of the user’s will and a system with its own inference processes, probabilistic outputs, and potential for error.
McKinsey calls this the “third actor problem” — a non-human entity initiating transactions that existing law was never designed to accommodate. The liability could fall on the consumer who delegated authority to the agent, the AI provider that built and operates it, the merchant that accepted the transaction, or the platform that facilitated it.
None of the parties in that liability chain are clearly responsible under existing frameworks. None of the frameworks were written with this architecture in mind. And the gap between what the law assumed and what is now happening in production systems is widening every month as AI companies compete to make their agents more autonomous, less interruptive, and faster at completing transactions without asking the user to confirm anything.
What the Law Currently Says — And Where It Goes Silent
The most relevant existing legal framework for agentic commerce in the United States is one that most practitioners have never had to think about seriously until now: the Uniform Electronic Transactions Act. UETA has been adopted by 49 states and the District of Columbia and serves as the legal framework for the use of electronic signatures and records, ensuring their validity and enforceability in interstate commerce.
UETA was written in 1999 to address a simpler version of the same problem: computers automating transactions without a human reviewing each step. Its drafters showed foresight by including provisions for “electronic agents,” which the statute defines as a computer program used independently to initiate an action or respond to electronic records in whole or in part, without review or action by an individual. Under UETA, in an automated transaction, a contract may be formed by the interaction of electronic agents of the parties, or by an electronic agent and an individual, even if no individual reviews or intervenes in each step of the process.
This means that today’s AI shopping agents almost certainly qualify as electronic agents under UETA, and the transactions they execute almost certainly create binding legal obligations on the users who deployed them. UETA generally holds users of electronic agents responsible for the actions of those agents, reinforcing that contracts can be formed through electronic agents even without user awareness of their actions.
That is the first uncomfortable truth of agentic commerce: the default legal position is that you are bound by what your agent does, whether or not you reviewed it.
But UETA also contains a significant consumer protection provision that becomes the central compliance obligation for every organization deploying transactional agents. UETA Section 10(b) provides a critical safeguard: if an electronic agent makes a mistake during a transaction that the user did not intend, and the user was not provided with a reasonable means to prevent or correct the error before it became irreversible, the user may void the transaction. Critically, this right to reverse the transaction cannot simply be waived by fine print in the terms of service.
Read that again carefully, because the compliance implication is profound: an organization cannot contractually eliminate a user’s right to void an erroneous agentic transaction. The only way to protect the transaction’s finality is to build genuine error prevention and correction mechanisms into the product itself. Terms of service that attempt to place all liability on the user are not just legally questionable — they may be unenforceable under the unconscionability doctrine that contract law already recognizes.
As desire for efficiency and speed increases and AI tools become more autonomous and familiar with their users, these inbuilt protections could start to wither away. Users who grow accustomed to their tool might find themselves approving transactions without vetting them carefully. This could lead to scenarios where the user seeks to void a transaction or, if that fails, attempts to shift liability to the AI tool’s developer.
The Privacy Dimensions Nobody Is Talking About Enough
Liability for erroneous purchases is the question that gets most of the attention in agentic commerce legal analysis. It should not be. The privacy dimensions of transactional agents are in many ways more significant, more novel, and more immediately demanding of compliance attention.
Consider what a transactional agent actually needs to function effectively. It needs access to the user’s payment credentials — credit card numbers, banking details, stored payment methods. It needs the user’s shipping addresses. It needs access to purchase history to infer preferences. It typically needs access to email or messaging platforms to receive order confirmations and track shipments. In more sophisticated implementations, it needs real-time access to the user’s calendar, location, or health data to make contextually appropriate purchasing decisions. And it operates across multiple third-party merchants and platforms, each with its own data practices, retention policies, and security posture.
The data profile of a transactional agent user is extraordinarily rich, extraordinarily sensitive, and extraordinarily widely distributed. Each merchant the agent transacts with receives payment data, shipping data, and behavioral signals. Each platform the agent operates through becomes a data processor with access to that profile. The AI provider building the agent has access to all of it.
GDPR governs how agents process personal data across multiple merchants in a single automated workflow. The Consumer Rights Directive dictates withdrawal and error handling but assumes a human initiated the transaction. The tension between those two regulatory expectations — a privacy framework built around informed human data subjects exercising meaningful control, and a commerce framework that assumes human initiation — is not resolved anywhere in current law. It is a gap that privacy professionals need to be actively bridging in their compliance programs right now.
The specific privacy issues that transactional agent deployments must address include:
Data minimization in a maximally-connected system. Transactional agents are structurally incentivized to access as much data as possible to make better purchasing decisions. The more context they have — spending history, dietary restrictions, size preferences, social calendar — the better they perform. But GDPR’s data minimization principle, the CCPA’s purpose limitation requirements, and equivalent frameworks require that personal data be limited to what is necessary for the stated purpose. The stated purpose of “completing purchases on the user’s behalf” could theoretically justify access to almost everything about a user’s life. Organizations need to define and technically enforce the boundaries of what data the agent actually needs, rather than relying on broad authorization grants that users provide during onboarding without fully understanding the implications.
Consent architecture for multi-party data flows. When a transactional agent completes a purchase, the user’s payment and shipping data flows from the AI platform to the merchant. That data flow is a third-party disclosure under most privacy frameworks, and it requires either explicit consent covering the specific merchant categories involved or a contractual framework that governs the disclosure. Most current transactional agent onboarding flows obtain broad authorization without specifically identifying the merchants or merchant categories to which data will be disclosed. That broad authorization is legally precarious, particularly in jurisdictions with specific requirements around third-party data sharing.
Inferred preferences as sensitive personal data. A transactional agent that operates over time builds an extraordinarily detailed picture of a user’s purchasing behavior, health-adjacent interests, family composition, financial situation, political affinities, and lifestyle. Those inferences — the model’s internal representation of who the user is — are personal data in most frameworks, regardless of whether they are ever explicitly stated or stored in a labeled form. Organizations need to assess whether their transactional agents are creating inference profiles that constitute sensitive personal data categories, and if so, what legal basis governs the processing of those inferences.
The fraud detection paradox. Complex systems are already in place designed to quickly identify and block fraudulent transactions, searching for deviations like purchases made in the middle of the night or from a location hundreds or even thousands of miles away from the customer’s home. Agentic commerce transactions could appear suspicious, as an agent might transact at odd hours or across geographies, or perform rapid repeated purchases in ways that resemble fraud bot behavior, resulting in purchase declines and undermining consumer trust. The privacy implication here is that legitimate agentic transactions will increasingly trigger anti-fraud systems that were designed to catch human behavioral anomalies. Addressing this requires either disclosing to merchants that a transaction is agent-initiated — which has its own privacy implications — or adjusting fraud models in ways that could create new exposure.
The “who owns the consumer relationship” question. The firms that define the trust, identity and liability frameworks for autonomous transactions will set the rules for the next era of commerce. When a user’s AI agent becomes the primary interface through which they interact with merchants, the agent provider sits between the consumer and the merchant in a way that fundamentally changes who controls the data, who owns the relationship, and who bears accountability for what happens to the consumer’s information. Amazon’s “Buy For Me” litigation with Perplexity illustrates how commercially contentious this ownership question has already become. The privacy implications — who gets to retain and use the behavioral data generated by agent-mediated transactions — are equally contentious and far less settled.
What Risk Management Actually Requires
For organizations building or deploying transactional agents, the risk management obligations are already clearly established by the intersection of UETA’s error correction mandate and privacy law’s data minimization and consent requirements. The question is not whether to implement them but how.
Build genuine error prevention into the product, not just the terms of service. UETA Section 10(b) cannot be contracted around. If businesses deploying AI agents want to ensure the transactions conducted by their agents are considered final and legally binding, they must build mechanisms that give the user a fair chance to catch and fix mistakes before they become irreversible problems. Confirmation prompts, purchase caps, category restrictions, and real-time notification systems are not optional features. They are legal necessities. And they must be implemented thoughtfully — a confirmation prompt that appears after a transaction has already been authorized, or one that times out and defaults to approval, does not satisfy the UETA requirement.
Implement authorization scope protocols. The emerging ecosystem of agent identity and authorization protocols — including efforts by Cloudflare, Google’s Agent Payment Protocol, and Mastercard’s agentic tokenization framework — provides technical infrastructure for communicating the scope of an agent’s authority to third parties. Ensuring that every player in a transaction can recognize when an AI agent is at work, and what that agent is authorized to do, is foundational to building trust in agentic commerce at scale. Organizations should evaluate and implement these protocols not merely as technical convenience but as legal risk management — an agent that exceeds its disclosed scope of authority has exposed both the user and the platform to liability.
Maintain comprehensive action logs. The ability to demonstrate, after the fact, that a user authorized a specific agent to take a specific action in a specific context is the primary defense against both UETA error-avoidance claims and regulatory investigations. Action logs that capture what the agent did, when, on what basis, and within what authorization scope serve simultaneously as a consumer trust mechanism, a legal defense document, and a compliance audit trail. They also enable privacy-compliant responses to data subject access requests — a user who asks “what data does your agent hold about me and what has it done on my behalf” should receive a complete, accurate, and timely answer.
Review contractual disclaimers for enforceability. The instinct to disclaim all responsibility for agent errors in terms of service is understandable but legally risky. Contractual disclaimers that completely allocate responsibility to users may raise enforceability issues under contract law’s unconscionability doctrine. Terms of service for transactional agents should be reviewed by counsel specifically for the enforceability of liability allocation provisions in the agentic context — not just adapted from existing e-commerce terms that were drafted with human-initiated transactions in mind.
Conduct a privacy impact assessment before deployment. A transactional agent that accesses payment credentials, purchase history, behavioral preferences, and third-party merchant data is processing personal data at a scale and sensitivity level that almost certainly triggers DPIA requirements under GDPR and equivalent requirements under an expanding range of state laws. That assessment needs to happen before deployment, not after the first consumer complaint.
The Regulatory Gap Will Not Last
As of early now, no jurisdiction has enacted regulation specifically addressing agentic commerce. The EU AI Act, the most ambitious AI regulation to date, predates the technology and contains no specific provisions for autonomous purchasing agents. The EU AI Act will enforce strict rules beginning August 2026, with potential fines up to 7% of global revenue for non-compliance. Regulations around AI-completed transactions, consumer protection, and liability remain in flux.
That regulatory vacuum will not persist. The combination of large-scale consumer adoption, documented error events, and the inherent difficulty of applying existing consumer protection frameworks to non-human transacting parties creates exactly the conditions that have historically prompted targeted legislative responses. The FTC’s existing authority over unfair and deceptive practices already applies to transactional agents — an agent that makes purchases consumers did not intend and provides no meaningful recourse is straightforwardly deceptive regardless of what the terms of service say. State attorneys general monitoring the rollout of these systems are watching for exactly these patterns.
The organizations that will fare best in that regulatory environment are the ones that do not wait for specific agentic commerce rules before implementing privacy-protective, user-empowering, error-correctable systems. The legal framework, imperfect and incomplete as it is, already tells them what they need to do. The commercial imperative to earn consumer trust in systems that spend their money without their real-time involvement reinforces it. And the privacy obligations that apply to the personal data those systems process were never optional in the first place.
Your AI may be doing the shopping. The accountability still belongs to you.
