A new kind of shopper showed up in 2026, and it isn't human. AI agents (the shopping modes in ChatGPT, Perplexity, and a growing list of assistants) now research products, compare options across stores, and in some cases complete the purchase, all without the person ever opening your website.
This changes what "being findable" means. For years the goal was to rank in Google and convert a human browsing your site. Now there's a second audience: software that reads your catalog programmatically and decides whether your product makes the shortlist. If an agent can't read and search your catalog, your products are invisible to it, no matter how good your SEO is.
This guide covers what AI agents actually need from your catalog, and how to expose it: structured product data they can read, and a search endpoint they can query.
How an AI Shopping Agent Finds Products
Picture a shopper who tells their assistant: "find me a waterproof barrel duffel under $120 for a kayaking trip." The agent doesn't browse. It does something closer to this:
- Reads product data from stores it can access, as structured machine-readable records (attributes, price, availability), not rendered HTML.
- Searches for products matching the intent, by meaning and increasingly by image, not by exact keyword.
- Ranks and shortlists based on how well each product fits the request and how complete the data is.
- Acts, either handing the shortlist back to the person or completing the purchase through an API.
Two of those steps depend entirely on your infrastructure: the agent has to be able to read your catalog, and it has to be able to search it. Get those two right and you're in the consideration set. Miss either and you're out.
Requirement 1: Structured Data the Agent Can Read
Agents don't want your product page. They want the facts behind it in a predictable format. That means structured data.
At minimum, expose for each product:
- A stable product ID or SKU
- Title and a real description (not marketing fluff, the actual attributes)
- Price and currency
- Availability and stock status
- Key attributes: material, dimensions, color, category, and anything specific to your vertical
- Image URLs
There are two common ways to surface this:
Schema.org markup on your pages. Add Product structured data (JSON-LD) so any agent crawling your site can parse it. This is table stakes and most ecommerce platforms support it.
A clean product feed or API. A JSON endpoint that returns your catalog as structured records. This is what agents prefer, because it's fast and unambiguous. If you already have a product feed for ads, you're most of the way there.
The principle is simple: anything an agent has to guess about your product is a reason to skip it in favor of a competitor whose data is explicit.
Requirement 2: A Search Endpoint the Agent Can Query
Structured data lets an agent read your catalog. But for an agent to find the right product out of thousands, it needs to search it the way it thinks: by intent and by image, not by exact keyword.
This is where most catalogs fall short. Default store search is keyword-based. An agent's query ("waterproof barrel duffel for kayaking") won't match a product titled "Voyager 60L Roll-Top Bag" through keywords, even though it's a perfect fit. Semantic search closes that gap because it matches meaning. Visual search closes the other half, because agents increasingly pass images ("find products like this photo") rather than text.
The practical move is to put your catalog behind a search API that handles both. Here's what that looks like with Vecstore.
Index each product once, carrying both its text and its image so it's findable either way:
await fetch(`${BASE_URL}/databases/${DB_ID}/documents`, {
method: 'POST',
headers: {
'X-API-Key': API_KEY,
'Content-Type': 'application/json',
},
body: JSON.stringify({
image_url: 'https://store.com/products/voyager-duffel.jpg',
text: 'Voyager 60L roll-top duffel, waterproof, marine-grade zipper',
metadata: {
sku: 'BAG-60L',
price: 109,
in_stock: true,
category: 'bags',
},
}),
});
Now expose a single search endpoint an agent can call with a natural-language query:
app.post('/agent/search', async (req, res) => {
const { query, top_k = 10 } = req.body;
const result = await fetch(`${BASE_URL}/databases/${DB_ID}/search`, {
method: 'POST',
headers: {
'X-API-Key': API_KEY,
'Content-Type': 'application/json',
},
body: JSON.stringify({ text: query, top_k }),
});
const { results } = await result.json();
// Return clean, structured records the agent can act on
res.json(results.map((r) => ({
sku: r.metadata.sku,
price: r.metadata.price,
in_stock: r.metadata.in_stock,
score: r.score,
})));
});
That's the whole interface an agent needs: send intent, get back structured, ranked products with the metadata required to make a decision. Because the same index also holds image embeddings, the agent can pass an image instead of text and the endpoint works the same way.
For a deeper walkthrough of the search side, see Ecommerce Search API: Add Visual and Semantic Search and How to Add a "Find Similar Products" Feature.
Make the Search Itself Agent-Friendly
A few details matter more for agents than for humans:
Return scores, not just results. Agents use the similarity score to decide confidence. A human eyeballs a grid; an agent needs the number.
Keep metadata complete and honest. Price, stock, and category in every record. An agent that finds a great match with no price will drop it for one that has it.
Return structured fields, not HTML. The endpoint above returns plain JSON, not a rendered card. That's what an agent can parse and act on.
Keep latency low. Agents often run many queries to fulfill one request. A slow endpoint gets timed out and skipped.
Why This Is Worth Doing Now
Agentic commerce is early, which is exactly why it's worth getting ahead of. The stores that are readable and searchable by agents today get included in shortlists while competitors are still serving keyword search and unstructured pages. The work also pays off immediately for your human shoppers, because semantic and visual search convert better than keyword search regardless of who (or what) is searching.
You don't need to rebuild your store. You need two things: structured product data exposed cleanly, and a search endpoint that understands meaning and images. The first is mostly configuration. The second is an API call.
The Bottom Line
The next wave of shoppers includes software that reads your catalog and decides whether your products qualify. Making your catalog agent-ready comes down to being readable (structured data) and searchable (semantic and visual search behind a clean API). Do both, and you're in the consideration set for human and agent shoppers alike.
Try Vecstore for free. 100 credits on signup, no credit card. Index a sample of your catalog and build an agent-ready search endpoint in an afternoon.
