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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA shopping agent is most useful when it knows where its help should stop. Searching, comparing and explaining trade-offs is one job. Choosing and buying on someone’s behalf is a different job, and the evidence available in 2026 suggests most consumers are comfortable with the first and far less comfortable with the second. The most valuable reply an agent can give is a specific, explained refusal: “I don’t have reliable information on this,” “this option breaks your stated limit,” “I need your approval before I buy,” or “I can’t verify this detail.”
The figures below come from the UK Competition and Markets Authority (CMA), Gartner’s 2026 surveys of U.S. consumers, and a YouGov survey of UK adults for ACI Worldwide. Each measures attitudes or stated expectations rather than observed purchases, and none tests whether refusing actually produces better outcomes. That gap is covered at the end.
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Four kinds of “no” worth building in
A refusal only helps if it tells the user why the agent stopped. Four reasons cover most cases where an agent should decline to decide, or should stop before acting. The list is an editorial framing of the safeguards the CMA describes (clear limits, transparency, and confirmation before consequential steps), not a regulatory standard.
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1. The agent lacks reliable information
Product data, stock levels, prices and specifications change, and some sources are incomplete or out of date. An agent that presents a guess as a fact is more dangerous than one that says it cannot tell. A useful reply names the gap: “The retailer’s listing does not state the battery capacity, and I could not confirm it from the manufacturer’s site.”
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2. The option fails the user’s stated constraints
If someone has said the budget is £300, the item must fit a specific size, or the device must work with an existing system, the agent should say when a candidate does not meet those terms. Silently ranking a non-matching product near the top is the failure readers are most likely to notice later.
3. The purchase needs explicit approval
Where an agent could place an order, change a subscription or use stored payment details, the final step should wait for a clear yes from the user. Asking for confirmation is not a sign of weakness; it is the mechanism that keeps the decision with the person who will live with it.
4. The agent cannot verify a consequential detail
Return policies, warranty terms, safety certifications and delivery deadlines can change the value of a purchase. If the agent cannot confirm one of these, it should say so before the user relies on it, not after an order has been placed.
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What shoppers say they want from an agent
Gartner’s January 2026 survey of 322 U.S. consumers is the clearest measure of willingness to delegate. Respondents were asked how much they would let AI narrow choices or make decisions in several categories. The figures are survey responses, not observed purchases.
| Question (Gartner, released May 27, 2026) | Share agreeing | Sample and scope |
|---|---|---|
| Willing to let AI narrow household-supply choices | 31% | 322 U.S. consumers, January 2026 |
| Willing to let AI narrow personal-electronics choices | 28% | 322 U.S. consumers, January 2026 |
| Willing to let AI make purchase decisions | Up to 11% across the lower-stakes categories covered | 322 U.S. consumers, January 2026 |
A separate Gartner survey of 846 U.S. consumers, fielded November–December 2025, asked people who had used AI while shopping for a recent purchase about their experience. Of those respondents, 54% said they had to double-check all the information generative AI provided, and 62% said that information wasted their time. These are the costs a poor agent imposes, and they are a reason an agent should label uncertain claims rather than deliver them with confidence.
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.”
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The UK trust gap
A YouGov online survey of 2,080 UK adults, fielded June 19–22, 2026 for ACI Worldwide, asked about trust in automated purchasing. Only 19% said they trusted AI assistants to follow rules for everyday purchasing decisions, compared with 55% who trusted a human expert or adviser. Sixty per cent said they would stop using an AI shopping agent after one mistake.
Those are self-reported attitudes in one country, and they do not show how people would behave after a real error. They do suggest that an agent’s first visible mistake carries a heavy penalty, which makes a candid refusal before the mistake more valuable than an eager answer that turns out to be wrong. The full release is available from ACI Worldwide’s investor news page.
Three levels of delegated authority
Most confusion about shopping agents comes from treating them as one thing. Separating three levels of delegated authority makes it clearer when a refusal is needed.
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| Level | What the agent does | Who decides and buys | What it must get right |
|---|---|---|---|
| Research and comparison | Finds options, checks listed details, compares prices and states uncertainty | The user chooses and buys | Accurate sourcing and clear flags on anything unverified |
| Bounded action with confirmation | Prepares or starts a limited step, such as adding items to a basket | The user approves each consequential step | Exactly what will happen before approval is requested |
| Autonomous purchase execution | Selects and buys within delegated rules | The agent, within limits set by the user | Spending limits, reliable product data, disclosure, oversight and a route to redress |
The CMA’s 2026 analysis of agentic AI describes current consumer-facing agents as mostly bounded: early shopping agents can search, compare and initiate simple actions with user confirmation, and end-to-end autonomous consumer decision-making remains limited. The full analysis is at GOV.UK: Agentic AI and consumers. The most important refusals therefore cluster at the second and third levels, where an agent moves from advice to action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the CMA expects from businesses
The CMA’s analysis puts the principle directly: “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.”
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Its guidance for businesses using AI agents, published separately, adds that responsibility does not move to a software supplier. In the CMA’s words: “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 says businesses remain responsible for what their AI agents do, as they are for employees, including when an outside provider designed or supplies the agent. This is UK guidance and applies to UK law; it should not be read as a global legal rule. The guidance is at GOV.UK: Complying with consumer law when using AI agents.
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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.
Safeguards named in the CMA’s analysis
The CMA’s analysis uses an illustrative personal shopping and finance agent to identify several safeguards:
- Clear disclosure of the agent’s limitations, incentives and affiliations.
- Mandatory confirmation before high-risk actions.
- Monitoring and human oversight.
- Audit logs and risk assessments.
These are presented as the CMA’s analysis and example rather than a complete technical checklist.
Practices in the CMA’s business guidance
- Consider relevant customer data and consent before the agent uses personal information.
- Be clear about AI use where it could affect a customer’s decision, and do not overstate what the system can do.
- Train agents to respect statutory and contractual rights and to avoid misleading customers.
- Test performance, monitor results, and refine the system quickly when problems emerge.
Ordering the checks before an agent answers
A refusal is easier to get right when the agent checks conditions in a fixed order. The sequence below applies the four kinds of “no” above to a typical request. It is an editorial suggestion, not a standard drawn from a regulator.
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- Identify the level of authority requested. If the user asks for a comparison, answer it. If the user asks the agent to buy, add to a basket, or change a subscription, move to step 2 before any action.
- Check the stated constraints. If a candidate breaks a budget, size, compatibility or delivery requirement, say so and drop it from the recommended list, or flag it clearly.
- Check whether key facts can be verified. Warranty length, returns terms, safety certification and stock status should be confirmed from the seller or manufacturer. If they cannot be, state the gap.
- Request approval for the consequential step. Show the exact item, price, seller, payment method and any recurring charge, and wait for an explicit yes.
- Close with what is known and what is not. Give the user the verified facts, the unverified points and the next step they can take, so the refusal leads to a decision rather than a dead end.
What the evidence does not show
None of the sources reviewed tests whether an agent’s refusal improves trust, satisfaction or purchase outcomes. The Gartner and YouGov figures measure attitudes at one moment; they do not track whether people who received refusals later bought better products or kept using the agent. The CMA analysis sets out principles and an illustrative example, not measured results from deployed agents.
The consumer surveys also differ in sample, country and category, so they should not be combined into one estimate. Gartner’s results are U.S. consumers across household and electronics categories; ACI and YouGov’s are UK adults. A Visa research page from 2025 covering U.S., Australian and New Zealand online shoppers points the same way, describing consumers who value efficiency alongside control and transparency, but it is a different market and survey design.
A fair test of the claim would compare outcomes for users whose agents refused unsuitable purchases with users whose agents did not, measuring regret, returns and continued use. Until such evidence exists, the defensible position is narrower: consumers say they want help searching and comparing, they want to keep the final decision, and they punish errors sharply. An agent that says “no” at the right moment is designed around those expectations.
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