Agentic commerce: how to prepare your store so AI recommends it

Agentic commerce is trade where an AI agent does the buying for a person: ChatGPT, Gemini or an assistant built into the browser or the operating system searches for products, compares offers and increasingly closes the transaction. You prepare your store for this not with a new campaign, but by making it readable to a machine: complete structured data (Product, Offer, price, availability), clean and complete product feeds, data available via API, credible reviews, and unambiguous prices and stock on a fast, stable site. Below is a concrete checklist, plus how to check whether AI already recommends you, before agent-driven traffic grows enough for the difference to show up in revenue.

What agentic commerce is, and why it is not another buzzword

In a classic online purchase a person types a query, scans results, opens pages, compares and clicks. In agentic commerce an AI agent acting for the buyer takes over that chain: it understands the intent (for example, "buy me road running shoes under 400 zloty with free returns"), searches offers, narrows them to a few, weighs them by price, availability and reviews, then presents a recommendation or places the order directly. The person approves the outcome rather than walking the whole path.

This is not a next-decade scenario. McKinsey estimates agentic commerce will influence 3-5 trillion USD of global retail by 2030, and a 2025 global Riskified study found that 73% of shoppers already use AI at some stage of buying, most often to suggest ideas, summarise reviews and compare prices. In 2026 Google, OpenAI and Shopify are introducing their own agentic commerce mechanisms that let an agent move from answer to purchase without leaving the assistant. The direction is clear: some buying decisions now happen where nobody looks at your site.

Zero-click: why your store must be readable to an agent

The key change hides in the word zero-click. An agent does not look at your banner, does not read an emotional headline and does not react to the colour of the "Add to cart" button. It reads data: product name, attributes, price, availability, ratings. If that data is not unambiguous and machine-readable, your product simply does not exist for the agent, however good it looks to a human. Just as indexability mattered in search, in agentic commerce what matters is whether a machine can read, without doubt, what you sell, for how much, and whether it is in stock.

The same mechanism governs visibility in generative answers more broadly, not only at the moment of purchase. We cover how to prepare a store for the models separately in our piece on GEO for eCommerce; agentic commerce is its transactional extension, where the stake is not a mention but a completed sale. So treat readability for agents as data hygiene, not as one more marketing channel to service.

The checklist: what to prepare so an agent picks you

The table below maps what an AI agent looks for during a purchase against what it needs from your store, with a concrete way to give it that. Treat it as a checklist and go row by row with your own store in hand.

Market figures: McKinsey (3-5 trillion USD impact by 2030) and the global Riskified 2025 study (73% of shoppers use AI). Accessed 2026-07-26.

Two rows deserve emphasis because they are most often neglected. The first is structured data: without correct Product and Offer, an agent guesses or skips your offer. How to build them and which mistakes to avoid, we lay out step by step in our piece on structured data for AI. The second is the feed and API availability: if all knowledge about a product lives only in the browser-rendered layer, the agent may never see it. We collected ready-made prompts, including for generating and checking structured data, in the AI Library, so this step goes faster than starting from scratch.

How to check whether AI recommends you: a new measurement habit

In classic SEO you watch rankings and search traffic. In agentic commerce you need a new habit, because a large share of interactions never reaches your analytics: the customer asks an agent, the agent reads the data and recommends or skips, and you see nothing in your store stats. So visibility in AI has to be measured actively, not waited for until it shows up in a traffic report.

The simplest start needs no tools. Write out a dozen or so real buying questions from your category, the kind a customer would ask (for example, "recommend a good manual espresso machine for home under 2000 zloty" or "where can I buy X with fast delivery"). Once a month, put the same questions to several models (ChatGPT, Gemini, Perplexity), and record whether your brand and products appear, how they are described, and whether the data is correct. That gives you a simple, repeatable measure: are you named or not, and is what the model says about you true.

That manual measurement can then be structured and run regularly rather than ad hoc. At Seedlight we do this as part of our AI Visibility (GEO) service: we systematically check how the models describe a brand and its products, where they cite stale data, and what is missing from the layer an agent reads. A caveat stated plainly, because it should be: nobody guarantees that a given model will recommend you, or how often, those are variables we do not control. What you do control is one thing, and it is a lot: whether your data is complete, unambiguous and available to a machine. That is the part worth finishing.

Where to start

The practical order is the reverse of the order of discovery: measure first, so you know where you actually stand, then fix the data starting with what matters most. Begin with Product and Offer structured data and one clean feed as the source of truth, because those decide whether an agent sees you at all. Then handle data availability via API or a stable feed, trust signals, and unambiguous prices and stock. Finish with speed and stability, because an agent that waits for a slow page simply picks a faster competitor.

If you are building or growing a platform, this readability for agents is cheapest to wire into the foundation rather than bolt on later with plugins. Seedlight builds eCommerce platforms on the BEAM framework so that structured data, feeds and performance are part of the architecture from the start, not a patch after the fact. Agentic commerce overturns none of this: it rewards stores that already have tidy data, unambiguous prices and a fast site. The good news is that preparing for agents is largely the same work that has helped humans buy from you more smoothly for years. Only now there is a machine on the other side too, and it does not forgive missing data.