AI shopping agents succeed or fail based on one thing, whether your product data is complete, accurate, and structured enough for a machine to read, trust, and act on. That is the work behind “getting your data right” for agentic commerce, and it starts well before any chatbot or orchestration layer is added on top.
According to Omar Qari, CEO of Logicbroker, agentic traffic lands almost entirely on two kinds of pages, search results and product detail pages, and it is growing far faster than traffic from human shoppers.
“When you hear everyone talking about getting your data right, this is what they’re talking about,” Qari says. “They’re talking about product data. They’re talking about the taxonomy, the semantic context, pricing, availability. These are the things the agents are going to need to make sure they’re landing on your pages.”
Most retailers have spent years optimizing those two page types for people. Agents read them differently, and the sections below describe what changes when the reader is a machine instead of a person.
What are the best practices for building an agent-ready product catalog?
Agents need the same information a knowledgeable salesperson would give a customer, just as structured data instead of conversation. In practice, that comes down to six things:
- Build a taxonomy deep enough that agents can narrow categories the way customers narrow intent.
- Hold attributes, including use case, occasion, fit, and compatibility, at the level of detail agents are actually asked about.
- Add semantic context so synonyms and related terms connect natural language to your catalog.
- Keep pricing and availability accurate in real time, since agents avoid products that look unavailable or inconsistently priced.
- Make the data machine-readable and crawlable, not just well-organized on the page.
- Treat supplier feeds as part of the same system, since gaps upstream carry straight through to your product pages.
Each of these gets a full breakdown later in this piece. The next two sections explain why agents need this level of detail, and what it costs you when they don’t have it.
Why does product data matter more to AI agents than to human shoppers?
A human shopper fills in gaps without noticing. They see a photo of a pair of jeans and know the fit, the rise, and roughly who they would suit. An agent sees only what your catalog says, and in most catalogs that is a product name, a size, a color, and a price.
The way people search with AI makes the gap wider. A keyword search might be “baggy jeans.” A request to an AI assistant sounds more like “low-rise baggy jeans for my wife that work for a casual dinner,” packed with context about use, occasion, and preference. Very few retailers hold attributes at that level of detail, so the agent has to guess, go looking elsewhere, or recommend a competitor whose data answers the question.
Where does thin product data break down?
Thin product data breaks agentic commerce in three places: search relevance, assistant cost and accuracy, and recommendation visibility.
Search returns the wrong products. At one major electronics retailer, a customer search for “Bluetooth speakers for beach” returned gaming controllers from a brand called Turtle Beach. “Beach” was not part of the retailer’s attribute set, so a keyword engine matched the only place the word appeared. Once the catalog connected “beach” to attributes like waterproof and portable, relevance improved and conversion followed.
Agents get expensive and inaccurate. A European department store launched an AI gift-finding assistant for the holiday season and shut it down three weeks later. Engagement and conversion were strong, but each customer question was costing around 50 cents.
The catalog held only basic attributes, so the assistant had to query product pages and spreadsheets in the background to assemble every answer, and those answers were often wrong. The assistant itself was easy to build. The data underneath it could not support it.
Products drop out of recommendations. AI shopping assistants are cautious about recommending anything they think a customer may not be able to buy. Testing has found that availability messaging such as “limited edition” can sharply reduce how often a product is recommended, and that AI assistants frequently give factually incorrect answers to shopping queries when product information is incomplete or inconsistent.
Accurate, current pricing and stock data protect your visibility as much as they protect the customer experience.
Why isn’t supplier data enough on its own?
For retailers selling extended ranges, much of the product data on the site comes from suppliers, and suppliers face a version of the same problem. One supplier working with dozens of retail partners has never had two retailers request the same product information for a new line. Every retailer asks for a different format, a different set of attributes, and a different level of detail.
That makes supplier data the right foundation rather than the finished product. Retailers get the most from it when they normalize it into their own taxonomy and then enrich it with the context their customers and AI agents actually look for. Logicbroker’s platform is built around this step: it normalizes incoming supplier feeds into a retailer’s taxonomy and helps enrich attributes with the semantic context agents need to match long, specific queries, a capability detailed on Logicbroker’s supplier management page.
How do you audit your product data for agentic commerce?
1. Taxonomy
Start here. Agents move through categories to narrow their options, so shallow or inconsistent categories make products harder to find and compare. Logicbroker’s documentation on configuring your taxonomy walks through how category structure gets set up inside the platform.
2. Attributes
Check whether your attributes match real queries. Agents match long, specific requests, and attributes covering use case, occasion, fit, and compatibility answer the questions customers actually ask. A short product name and a size field are not enough.
3. Semantic context
Semantic context builds on attributes. Synonyms and related terms, such as linking “beach” to “waterproof,” let an agent connect natural language to your catalog instead of matching on a literal keyword.
4. Pricing and availability
Pricing and availability need to be accurate in real time. Agents steer away from products that look unavailable or inconsistently priced, so stale data quietly removes you from the recommendation set even when the product itself is in stock.
5. Machine-readability
Even well-structured data is invisible if a bot can’t crawl the page or parse the format it’s in. A practical checklist for this step includes:
- Marking up product pages with Schema.org Product attributes and following Google’s product structured data guidelines so pricing, availability, and specifications are legible to machines, not just humans.
- Confirming AI crawlers can actually reach your product pages, since Stripe’s guidance on preparing for agentic commerce notes that agents need to query product and inventory data directly rather than infer it from a rendered page.
- Keeping consistent product identifiers, such as SKUs or GTINs, so an agent can match the same item across your site, your supplier feeds, and any external listings.
- Serving catalog, pricing, and inventory data through a structured feed or API rather than a static page alone. Rather than agents scraping pages and guessing at what they find, Logicbroker exposes this data directly to agents through a Model Context Protocol (MCP) server, which keeps the information authoritative and current. The mechanics of that integration are explained on Logicbroker’s Agentic Commerce FAQ.
6. Supplier feed consistency
Gaps and format differences in supplier data carry straight through to your product pages, so the audit has to reach upstream as well. Logicbroker’s guidance on how to use supplier catalogs covers how incoming feeds get reconciled before they reach the storefront.
A practical starting point for all six of these is your own site search data. The queries customers type, especially the ones that return poor results or no results at all, show where your attributes fall short of how people describe what they want. Logicbroker’s resource library has more on turning that kind of signal into a structured action plan.
What knowledge is missing from your systems entirely?
Some of the most important product and operational context in any retail business has never been written down. It sits with experienced merchandisers, category managers, and operations staff who know which supplier data needs correcting and which products customers confuse.
“Your operations probably rely on these unsung heroes in your organization, people who have this information in their heads, and they are just quietly compensating for the gaps,” Qari says. “Strengthening the foundation is about how we can translate that into systems and into data.”
Agents can only work with the context they are given. Capturing that knowledge in your catalog, your taxonomy, and your supplier data standards is what allows them to represent your products the way your best people would.
How do you get your catalog agent-ready?
Retailers that win in agentic commerce will not necessarily have the most sophisticated AI. They will have product data complete and accurate enough for any agent to read, trust, and recommend. That work starts with an honest audit of what your catalog says today, and it compounds every time a supplier feed, a category, or a search query is improved. Logicbroker’s case studies show what that work looks like once it’s underway, and the Agentic Commerce Survey tracks how shopping behavior continues to shift toward agents.
This content breaks down Omar Qari’s talk at The Vision Summit 2026 in London. Visit the official Vision Summit 2026 Insights Playbook to learn more about the event.