Agentic commerce is buying and selling carried out by AI agents that can take actions, not just answer questions. Instead of a shopper clicking through category pages and a support bot pasting help-center links, an AI agent holds the conversation and completes the task: it finds the right product, applies the right policy, places or changes the order, and resolves what comes after. The term covers two very different sides of the same shift, and retail teams need a position on both.
What does "agentic" actually mean here?
An AI system is agentic when it can decide on a next step and execute it, within rules someone gave it. A chatbot answers; an AI agent acts. In commerce that means checking a live order, initiating an exchange inside the return policy, recommending a product from the actual catalogue and current stock, or escalating to a person when the situation calls for one. The distinction matters because acting requires two things answering never did: integrations into the systems where orders and policies live, and a control layer that keeps a probabilistic model from improvising about money. That second requirement is where most early projects quietly failed, and it is the reason the architecture question now leads most serious evaluations.
Buy-side and sell-side: the two halves of agentic commerce
The buy-side is the consumer's AI doing the shopping. ChatGPT can already search products and complete purchases for its users, and the major assistants are all moving the same direction. Brands do not operate these agents; they get discovered (or skipped) by them.
The sell-side is the brand's own AI agent, deployed on its site and channels, that sells to shoppers and resolves their customer service. This is the half a retailer actually controls: its catalogue knowledge, its policies, its tone, its guardrails.
The two halves will meet - a consumer's assistant negotiating with a brand's assistant is the logical endpoint - but today they mature at different speeds. Buy-side reach belongs to the platform companies. Sell-side results already exist in production, and they are measurable.
What does sell-side agentic commerce look like in production?
Total Wine & More, the largest independent fine-wine retailer in the US with 294 superstores and roughly 8,000 wines per store, runs a Zowie AI agent that works both ends: it resolved 64% of customer conversations on its own, and shoppers who engage the AI agent convert at four times the rate of a traditional session, spending about 20% more per order when they buy.
The scoping matters and is worth repeating precisely: that is 4x conversion among shoppers who engage the AI agent versus a traditional session, not a sitewide lift. The honest version of the claim is still remarkable, because it shows what the conversation itself does to a buying decision.
Cole Lillie, Total Wine's Director of Digital Product Management, put the surprise plainly: "I knew it could answer a question for a customer. I didn't think it would change what they buy." Customers were buying products they had not known existed, because they learned about them in conversation. That discovery effect is the engine of the sell-side story.
How is agentic commerce different from conversational commerce?
Conversational commerce is the older, broader idea: commerce happening through conversation (chat, messaging, voice) rather than through navigation. Agentic commerce is what conversational commerce becomes when the system on the other end can genuinely act: check, decide, execute, and prove what it did. Every agentic deployment is conversational; plenty of conversational deployments were never agentic, which is why so many chat widgets of the last decade left no mark on revenue. We keep full definitions and history in What is conversational commerce.
What changes for a retail brand?
Three things, in rough order of arrival:
The search bar stops being the ceiling of online service. In a store, your customer gets an expert; online, they get a search bar and a filter sidebar. An AI agent closes that gap, which is why the first visible effect in production is discovery: shoppers finding products they would never have typed into search.
Service and selling stop being separate systems. The same conversation that resolves a delivery question can, where rules allow it, turn a refund request into an exchange, or a product question into an order. Retailers that treat the AI agent as a revenue channel and a service operation in one are the ones reporting numbers.
Trust becomes an architecture question. One invented discount on a live conversation is a margin incident. Production systems separate the talking from the deciding: in Zowie's case, the Decision Engine executes prices, discounts, return eligibility and age checks deterministically while the model handles the language, and every decision is logged and reviewable.
How should a brand prepare for agentic commerce?
A realistic readiness list looks like this: policies written somewhere a system can execute them (return windows, exchange rules, promotion limits, ID checks); integrations into the order stack so the AI agent can act rather than apologize; quality oversight built for volume, meaning every conversation evaluated rather than a sampled few percent; and a sequencing plan. The pattern from production deployments is service first, selling second: the AI agent earns trust by resolving customer service to a measured standard, then switches on selling. That order is also the internal political path of least resistance, because it proves control before it asks anyone to trust the system with revenue.
Want to see agentic commerce rather than read about it? Watch a real shopping conversation run at getzowie.com/commerce, or read how Total Wine runs it in production.
