Imagine a shopper who never even visits your homepage. Instead, they ask an AI assistant for a recommendation and get a shortlist of three products from different stores within seconds. Your product could be on that list, or it could be left out entirely, without you ever knowing why.
AI shopping agents are making this a reality, and October brought a clear example of how quickly things are changing. Walmart announced its Agent Gateway and Agent Profiles, giving customers more control over how external AI agents interact with Walmart and Sam’s Club. Customers can set spending limits, decide which actions an agent can take, require approval before a purchase, and revoke access whenever they choose.
Amazon went the other direction. Less than two weeks after Meta launched its Muse agent, Amazon blocked it from browsing and buying on its platform. The company said it hadn’t authorized Muse to access customer accounts and pointed to security and user experience.
Shoppers themselves are wary. According to the 2026 Commerce Relevance Report, only 16% feel comfortable letting an AI assistant find and buy products for them. But an agent doesn’t have to press “buy” to matter. It’s already doing the research, and research is where most products get cut from consideration.
So the question for any retailer is pretty basic. If an agent lands on your store, can it figure out what you sell? Here are seven things worth fixing before the holiday rush.
1. Treat Product Data as the Storefront Agents See
Your homepage banner took weeks to design. An AI agent won’t look at it.
Agents care about details they can read and compare. Say someone asks for “waterproof hiking boots, wide fit, under $150, delivered by Friday.” The agent needs four facts from each product page: is it waterproof, does it come in wide, what does it cost, and when will it arrive. If “wide fit” only shows up in a photo of someone on a trail, the agent can’t see it. Your boot quietly drops out.
Try this with your top 100 sellers. Could someone who has never seen the product answer the usual buying questions from the attributes alone? Wherever the answer is no, you’ve found a spot where agents will pass you by.
2. Close Attribute Gaps Across the Whole Catalog
Most stores won’t pass that test, and it’s rarely anyone’s fault. Supplier feeds show up half empty. Adding material, size, color, and compatibility to thousands of SKUs by hand is the kind of job that sits on a to-do list for years.
Automation finally makes this realistic. Platforms like Experro offer AI product data enrichment software that reads product images, names, categories, and descriptions, flags what’s missing or inconsistent, and fills in attributes based on an industry taxonomy. The same data also powers your filters and on-site search, so human shoppers get the benefit too.
One word of caution. Have someone check high-stakes fields, like safety ratings, exact dimensions, and part compatibility, before they go live. A missing attribute loses you a match. A wrong one can lose you a customer.
3. Keep Prices and Stock Accurate in Real Time
People forgive a lot. If a price changed overnight, most shoppers sigh and move on. An agent in the middle of a purchase doesn’t have that patience. When your feed says “in stock” and checkout says “sold out,” the order fails and your store looks unreliable.
Walmart seems to know this. It has said prices shown to an AI agent will match what a human sees on Walmart.com, with offers and fees disclosed before purchase.
For smaller retailers, the work here is unglamorous. Sync pricing and inventory more often during peak weeks. Show shipping and fees early rather than springing them at checkout. And take discontinued products down instead of leaving them up with a grayed-out button.
4. Write Product Descriptions That Answer Real Questions
Nobody talks to an AI assistant in keywords. They say things like “I need a quiet blender for a tiny apartment” or “what’s a good gift for someone just starting to paint?”
Now look at a typical product description: “Premium quality. Stunning design. Built to last.” None of that helps an agent connect your blender to the tiny-apartment shopper. Copy that says who the product is for, what problem it solves, and how it differs from the one next to it gives the agent something to work with.
A quick check: read a description and count how many real questions it answers. If it’s two or fewer, the page is leaning on photos that agents can’t read.
5. Decide Your Agent Policy Before Holiday Traffic Peaks
Walmart and Amazon have drawn two very different maps for agentic commerce. Walmart is letting shoppers bring their own agents. Amazon prefers its own assistant and is keeping outside ones out, at least for now.
Most mid-size retailers haven’t picked a side. In practice, though, their bot filters, login walls, and fraud rules are already picking one for them. That’s a strange way to make a decision this big, especially weeks before the busiest stretch of the year.
It’s worth getting your eCommerce, security, and payments people in a room for an hour. Decide which agents can browse and which can actually check out. Work out how agent visits will show up in your reports. And figure out what should happen when an agent runs into a CAPTCHA, because it will.
6. Make On-Site Search Match the Way Agents Ask
Agents rarely take a shopper all the way to checkout. More often, someone uses AI to narrow down options, then clicks through to the store to double-check the details. That last stop matters. If your search bar chokes on the same kind of question that brought them there, you lose them.
Your search logs will tell you how big the problem is. Look for long, chatty queries and searches that come back empty. Something like “dress for an outdoor wedding in August” is exactly how agent-referred shoppers search. If that returns nothing, you have a leak.
Search tools that understand intent, not just exact words, fix a lot of this. A shopper who arrives from an AI assistant should find roughly the same answer on your site that the assistant gave them.
7. Measure the Traffic Agents Send
You can’t improve what you can’t see. Separate visits referred by AI platforms in your analytics and compare them with search and email traffic. Look at how often they convert, how much they spend, and how often they return things.
Then go look for yourself. Once a month, have someone ask the big AI assistants the questions your customers ask most. Write down which products come up, yours and everyone else’s. It doesn’t take long to spot a pattern, and when your product gets skipped, the cause is usually a missing attribute or a vague description.
This also gives you something concrete to show leadership, instead of asking them to fund “AI readiness” on faith.
Where This Leaves Retailers
AI agents won’t replace human shoppers this holiday season. Most people still want to make the final call. But agents are already deciding which products people see when they ask, and that role will probably grow.
What I like about this list is that none of it is wasted if adoption turns out slower than the headlines suggest. Better product data, honest pricing, and search that gets what people mean make a store easier to shop, whether the visitor is a person or a piece of software. If you only do one thing this month, start with the catalog.