Make the agent stop before checkout, inspect the exact order it proposes, and approve that order yourself before it is submitted. Check the product and variant, seller, quantity, full price, delivery address, and any subscription or add-on. If one of those details changes after you approve, require a fresh review. This reduces risk; it cannot guarantee an agent will never make a mistake.
Put a human approval gate before checkout
Let the agent search, compare, and prepare a cart, but keep the authority to submit payment separate. The approval should happen at the final action—not just when the agent recommends a product. OpenAI describes this safeguard for its computer-using agent: “The CUA model is trained to ask for user confirmation before finalizing tasks with external side effects, for example before submitting an order, sending an email, etc.” That describes OpenAI’s system, not every shopping agent. OpenAI’s computer-using agent announcement also discusses user supervision on sensitive sites and risks such as prompt injection, jailbreaks, and phishing.
What to check in the order preview
- Product identity: confirm the exact model, size, color, compatibility, or other variant—not merely the product category or image.
- Quantity: check the number of units and whether the order repeats.
- Merchant: verify the seller shown at checkout, especially if a marketplace listing has multiple sellers.
- Total: check the amount due, including shipping, taxes, and fees shown before submission.
- Destination: confirm the delivery address and any delivery option that matters.
- Extras: look for subscriptions, warranties, bundled items, or other add-ons.
This checklist applies the exact-action approval principle to a practical purchase review; OWASP does not prescribe this particular shopping-field list.
Approve only the order you saw
Approval should be tied to the displayed order. If the product, quantity, seller, price, or delivery destination changes after review, treat the revised cart as a new action and inspect it again. A confirmation for one set of order details should not silently authorize a different one. OWASP recommends binding approval for high-impact actions to the actor, tool, target, normalized parameters, time, and expiry, with replay protection. OWASP’s AI Agent Security Cheat Sheet sets out these controls.
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Reduce the chance of a bad choice before the cart
Replace vague goals with constraints
“Buy the best one” leaves room for an agent to make assumptions about price, quality, seller, or acceptable substitutes. Give it constraints it can apply while searching: required product features, a price ceiling, acceptable merchants, and whether it may choose a substitute. These instructions narrow the search; they do not replace checking the final cart.
Treat web pages as untrusted input
Product descriptions, ads, reviews, and other page text can contain instructions that conflict with what you asked the agent to do. OpenAI identifies website-originating prompt injection, jailbreaks, and phishing as risks for computer-using agents. Do not assume that a product page or a reassuring agent response makes an order safe. Keep a confirmation step between the agent’s browsing and any purchase submission.
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What agent operators should enforce in software
If you operate or build an agent, a conversational instruction such as “ask before buying” should not be the only barrier. OWASP recommends an independent policy or execution component that checks the proposed action’s scope, privileges, and approval state. For purchases, that means validating the actual order parameters before allowing submission, rejecting stale or mismatched approvals, recording actions for audit, and failing closed when validation or approval checks fail.
Consumer-side review and system-side enforcement address different risks. A shopper can catch visible mismatches in a meaningful preview; an operator’s execution boundary can block an unauthorized or altered action even if the agent proposes it. The former depends on the person seeing and checking the details, while the latter requires implementation by the agent provider or operator. Neither should be mistaken for a guarantee of error-free shopping.
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Monitor agent behavior and respond to mistakes
For business use, the UK Competition and Markets Authority says consumer-law responsibilities apply whether a business engages customers through people or AI. Its guidance recommends checking that results are correct and behavior is intended, maintaining active human oversight, and quickly refining prompts or workflows when problems arise. These legal and operational points are UK-specific; businesses elsewhere should check the rules that apply in their jurisdictions. CMA guidance on using AI agents.
How much autonomy shopping agents have today
UK Department for Business and Trade research describes consumer shopping agents as relatively bounded today: they may search, compare, and initiate simple actions with user confirmation, while fully autonomous consumer use remains limited. The report also notes uncertainty about how quickly more autonomous systems will emerge. That is a qualified description of the landscape, not a claim that every product works the same way or is available in every country. UK government research on agentic AI and consumers.
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Commerce infrastructure can help systems communicate about agent identity and intent, but it is not a substitute for reviewing the purchase. Visa’s 2025 Trusted Agent Protocol announcement describes a protocol carrying agent intent, consumer-recognition information, and optional payment information. The announcement does not promise to prevent wrong-item purchases. Visa’s Trusted Agent Protocol announcement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the question is becoming more relevant
Adobe Digital Insights reported that traffic from generative-AI sources to U.S. retail websites grew 4,700% year over year in July 2025, based on direct transaction data covering more than one trillion visits to U.S. retail sites. That measures attributed visits, not purchases autonomously completed by agents. Adobe also reported that 38% of surveyed U.S. consumers had used generative AI for online shopping and 52% planned to do so that year; those are findings from its survey of 5,000 U.S. consumers, not a population census or a count of agent purchases. Adobe Digital Insights’ 2025 shopping findings.
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