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A shopping agent earns trust most often by declining to act. The useful refusals are the ones that stop an agent from buying when it cannot confirm that a product fits what you asked for, when it lacks authority to spend your money, or when a consequential detail cannot be checked. A good “no” also tells you why, so you know what to do next. Recent evidence, mostly UK regulatory guidance and surveys published in 2026, points the same way: shoppers want help finding, comparing and checking products, and most want to keep the final purchase decision themselves.
What a useful “no” has to say
A refusal that stops at “I can’t help with that” leaves the user guessing. A useful one identifies the kind of problem, and that tells the user whether to rephrase the request, supply missing information, or take over the decision. The four categories below are an editorial framework built on the safeguards that UK regulators describe (boundaries, transparency and confirmation). They are not a formal standard set by any regulator.
“I don’t have reliable information”
The agent could not retrieve current price, stock, specification or seller data it can stand behind. The useful response names the gap, such as “I could not confirm the warranty terms on this listing,” and does not fill the gap with a plausible guess. This matters because product data changes quickly and listings are often incomplete.
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The agent found a product, but it breaks a rule the user set: a budget ceiling, a required certification, a size, or a delivery date. A good agent says which constraint fails and by how much, rather than quietly widening the search or ranking the item near the top anyway.
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“This purchase needs your explicit approval”
The action is consequential enough that the agent should stop and ask, even if it was technically able to complete it. Examples include a payment, a subscription that renews, a change to a saved payment method, or any purchase above a limit the user agreed to in advance.
“I can’t verify this detail”
The agent has what looks like the right answer, but it cannot check a fact that matters to the decision, such as whether a seller is the manufacturer’s authorised reseller or whether a discount is still valid at checkout. Saying so is more useful than presenting an unverified claim with confidence.
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Why shoppers are cautious about handing over the decision
Survey evidence from 2026 shows a consistent pattern: consumers accept help with information but resist delegation. The table below lists the figures reviewed for this article. Each one carries its own sample, field dates and question wording, so they should not be merged into a single estimate.
| Source and date | Sample and fieldwork | Finding | What it measures |
|---|---|---|---|
| Gartner, released May 27, 2026 | 322 U.S. consumers, January 2026 | 31% willing to let AI narrow household-supply choices; 28% for personal-electronics choices | Stated willingness in a survey, not observed purchases |
| Gartner, released May 27, 2026 | Same release: lower-stakes categories covered | Maximum of 11% willing to let AI make purchase decisions | Stated willingness, not observed purchases |
| Gartner, released May 27, 2026 | 846 U.S. consumers, fielded November–December 2025; respondents who used AI while shopping for a recent purchase | 54% had to double-check all information GenAI provided; 62% said the information wasted their time | Self-reported experience of AI shopping help |
| YouGov for ACI Worldwide, released June 29, 2026 | 2,080 UK adults, fielded June 19–22, 2026 | 19% trusted AI assistants to follow rules for everyday purchasing decisions, versus 55% for a human expert or adviser | Self-reported attitudes, not a shopping trial |
| YouGov for ACI Worldwide, released June 29, 2026 | Same UK sample and field dates | 60% said they would stop using an AI shopping agent after one mistake | Self-reported stated intention, not observed behaviour |
Gartner’s VP Analyst Kate Muhl summarised the pattern this way: “Consumers are not looking to outsource shopping decisions to AI. They want AI to help them find better information, compare prices, identify deals and narrow choices, while keeping final decision-making control for themselves.” That statement appears in Gartner’s May 27, 2026 press release.
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- Interior Zipper Pouch for Secure Storage: Keep small essentials neatly contained with the built-in inner zipper pocket. Perfect for keys, coupons, receipts, phone, or loose items, this secure compartment prevents shifting and keeps valuables protected while you shop.
- Eco-Friendly & Reusable: A sustainable alternative to single-use plastic bags, designed to reduce waste and support environmentally conscious shopping. Built for long-term use, this reusable system helps you minimize your environmental footprint with every grocery trip.
- Heavy-Duty & Extra Spacious: Made from premium 210D polyester with reinforced double-stitched seams and handles for superior strength and long-lasting durability. Each bag is built to support up to 40 lbs, making it ideal for heavy grocery hauls, bulk shopping trips, and warehouse store runs.
The ACI Worldwide figures point to a sharper problem. A single error is enough for many UK respondents to stop using an agent, and the trust gap with a human adviser is wide. Both figures come from stated intentions, so they show how people say they would react rather than how they have reacted. The full release is available from ACI Worldwide’s investor site.
Three levels of agent authority
The right place for a “no” depends on how much the agent is allowed to do. Shopping agents fall into three broad levels. The comparison below describes the levels, not any named product, because the sources reviewed do not rank specific shopping agents.
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| Level | What the agent does | Does it need approval? | Main risk | Where a “no” belongs |
|---|---|---|---|---|
| Research and comparison | Finds options, checks listed details, compares prices and explains uncertainty | No approval is needed, because the user chooses and buys | Inaccurate or incomplete product data that the user trusts too much | When it cannot verify a detail or a listing fails stated constraints |
| Bounded action with confirmation | Prepares or starts a limited step, such as adding an item to a basket | Yes, before any consequential step | A step taken without the user noticing its effect | When a step would go past what the user approved |
| Autonomous purchase execution | Selects and buys within delegated rules | Only what the rules themselves require | Spending beyond intent, poor product data, and unclear redress | Whenever a rule is missing, ambiguous or cannot be checked |
The UK Competition and Markets Authority (CMA) describes the current state of the market in similar terms. In its 2026 analysis of agentic AI and consumers, it says early consumer-facing shopping agents can search, compare and start simple actions with user confirmation, and that end-to-end autonomous decision-making by consumers remains limited. The most demanding level, where an agent buys on its own, is therefore the least common.
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What regulators expect from businesses
The CMA’s analysis sets the test in one sentence: “Agentic AI will deliver greatest consumer value and be trusted when autonomy is bounded clearly by user intent and backed by strong transparency and accountability.” That sentence is the core of the argument for a well-placed “no.”
Best Value
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- Easy, Organized Shopping: Designed to attach directly to your grocery cart, these reusable bags keep items upright, visible, and neatly separated throughout your trip. Load as you shop, streamline checkout, and lift each bag straight into your trunk for a faster, more efficient grocery run.
- Interior Zipper Pouch for Secure Storage: Keep small essentials neatly contained with the built-in inner zipper pocket. Perfect for keys, coupons, receipts, phone, or loose items, this secure compartment prevents shifting and keeps valuables protected while you shop.
- Eco-Friendly & Reusable: A sustainable alternative to single-use plastic bags, designed to reduce waste and support environmentally conscious shopping. Built for long-term use, this reusable system helps you minimize your environmental footprint with every grocery trip.
- Heavy-Duty & Extra Spacious: Made from premium 210D polyester with reinforced double-stitched seams and handles for superior strength and long-lasting durability. Each bag is built to support up to 40 lbs, making it ideal for heavy grocery hauls, bulk shopping trips, and warehouse store runs.
The CMA’s separate guidance for businesses using AI agents places responsibility squarely on the business. It states: “Ultimately, you will be responsible if an AI agent does something illegal, so it is important to make sure you think about compliance with consumer law from the start.” The guidance also says businesses remain responsible for what their agents do, as they are for employees, even when an outside provider designed or supplies the agent. This is UK guidance. It does not establish a global legal rule.
Business duties in the 2026 guidance
- Be clear about AI use wherever it could affect a customer’s decision, and do not overstate what the system can do.
- Consider relevant customer data and consent before the agent uses it.
- Train agents to respect statutory and contractual rights, and to avoid misleading customers.
- Test performance, monitor results and keep human oversight in place.
- Refine the system quickly when problems emerge.
Safeguards in the CMA’s illustrative agent
The CMA’s broader analysis uses an illustrative personal shopping and finance agent to show what protections look like in practice. It lists five: clear disclosure of limitations, incentives and affiliations; mandatory confirmation for high-risk actions; monitoring and human oversight; audit logs; and risk assessments. These are the CMA’s example, not a technical checklist that every product must follow, and they do not describe the law in other countries.
Designing a refusal that helps
A refusal is only useful if the user can act on it. A well-built agent follows a consistent sequence:
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- State which of the four categories applies, in plain language.
- Name the specific constraint, data point or approval that is missing.
- Offer the next step, such as a request for the user’s budget, a link to the seller’s own warranty page, or a prompt to confirm the purchase.
- Record the decision so the user can see what the agent declined and why, which is consistent with the audit-log safeguard the CMA describes.
What the evidence does not show
The sources establish that shoppers value help with research and comparison, want to keep final control, and say they would distrust an agent after a mistake. They do not establish that an agent which refuses more often produces better purchases or greater trust. No study reviewed for this article tested refusal against outcomes. The survey figures are self-reported, and the 60% figure concerns stated intentions in one UK sample.
Scope also matters. The Gartner data covers U.S. consumers, the ACI Worldwide data covers UK adults, and the CMA guidance applies in the UK. A 2025 Visa study of online shoppers in the United States, Australia and New Zealand supports the broader point that shoppers value efficiency alongside control and transparency, but its findings should be read within those markets. Each source should be cited with its own date and sample.
Quick Recap
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