{"id":2645,"date":"2026-08-20T12:39:38","date_gmt":"2026-08-20T10:39:38","guid":{"rendered":"https:\/\/mairateam.com\/?post_type=glossary&p=2645"},"modified":"2026-08-20T15:55:24","modified_gmt":"2026-08-20T13:55:24","slug":"agentic-commerce","status":"publish","type":"glossary","link":"https:\/\/mairateam.com\/en\/glossary\/agentic-commerce\/","title":{"rendered":"Agentic commerce"},"content":{"rendered":"

Agentic commerce<\/strong> is a purchase carried out by an AI agent on a person's behalf. The agent finds the product, compares offers against the stated conditions and, in its more advanced form, completes the order. For an online shop that changes something fundamental: the outcome is decided not by the design of the site or the campaign, but by the quality and machine readability of the product data.<\/p>\n

In short<\/h2>\n\n\n\n\n\n\n\n
What it is<\/strong><\/td>\nSearch, comparison and possibly checkout performed by an AI agent instead of the user<\/td>\n<\/tr>\n
How mature it is<\/strong><\/td>\nSearch and comparison work today, automated payment is still limited and differs by platform<\/td>\n<\/tr>\n
Who is affected first<\/strong><\/td>\nCategories with clear parameters: electronics, spare parts, consumables, travel, insurance<\/td>\n<\/tr>\n
What decides<\/strong><\/td>\nCompleteness and accuracy of product data, feed availability, consistent price and stock across sources<\/td>\n<\/tr>\n
What loses weight<\/strong><\/td>\nVisual design, banner creative and part of classic checkout UX work<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

How an agentic purchase runs<\/h2>\n

The user states an intent, not a query. Instead of \"men's running shoes size 10\" they write \"I need running shoes for asphalt, size 10, under 150 euros, delivered by Friday\". The agent breaks the intent into conditions, retrieves offers, compares them and returns a shortlist or a direct recommendation.<\/p>\n

The data layer decides, not the page<\/h3>\n

An agent does not read a product page the way a person does. It takes structured data, the feed, and whatever it finds about the product on comparison sites and marketplaces. When the data is missing a material, a dimension or a delivery window, the product drops out of the shortlist even if it is objectively the best option.<\/p>\n

Inconsistent data costs more than it used to<\/h3>\n

A price that differs between the site, the feed and a marketplace used to be an operational annoyance. For an agent it is a reason to discard the offer, because it cannot verify it.<\/p>\n

The brand does not disappear from the decision<\/h3>\n

Agents work with reviews, ratings and how the brand is written about. A brand with no trace in the data carries less weight than one with a consistent public record.<\/p>\n

From our own practice: conversational attributes in Merchant Center<\/h2>\n

The closest practical step we already take in client accounts is conversational attributes in Google Merchant Center. These extend product data with the properties people ask about in sentences rather than parameters: what the product is for, what it suits, what the package contains.<\/p>\n

They are filled in as sentences, and that sentence is what the model matches the customer's question against. On large catalogs the attributes cannot be written by hand: they are generated by rules over the feed and templates per category, otherwise the work never finishes.<\/p>\n

The second thing we see in day-to-day operations: on accounts where we manage large catalogs, the most frequent reason a product fails to show is a missing or inconsistent attribute, not a low bid. The pattern holds across clients. Agentic commerce will only make that problem larger.<\/p>\n

Common mistakes<\/h2>\n