An agentic storefront is the infrastructure that surfaces a brand’s product library in AI platforms and allows customers to buy products from AI chat interfaces. It functions as a sales channel separate from your existing website, retail stores, marketplaces, and app.
Agentic storefronts are different from AI shopping agents, which answer customer questions and provide recommendations from your website. This is defined as agentic commerce , a broader category of AI shopping that includes branded website shopping agents.
How can brands create agentic storefronts?
Currently, agentic storefronts for ChatGPT are available to eligible Shopify merchants. Other agentic storefronts, such as Microsoft Copilot, Google AI Mode, and Gemini, are only available in early access for some Shopify merchants.
To use a Shopify agentic storefront, a brand is required to:
- Sell products that are eligible for inclusion in the Shopify catalog.
- Have complete and visible terms of service, privacy, and return and refund policies.
- Allow guest check-out.
As merchants become eligible, they can manage where they offer direct selling through an agentic storefront. All Shopify functions—shipping, discount, cart, and check-out validation—will run as usual in agentic storefront check-outs.
As you prepare to sell on agentic storefronts, Shopify catalog mapping can help you structure your product data to show up on AI channels by mapping it to your preferred sources. This will help make sure your products display correctly in AI search results and in agentic storefronts without affecting your store’s unique product structure or data.
What does an agentic storefront shopping experience look like?
With agentic storefronts, customers can complete purchases without leaving their AI chats. Here’s how:
1. Search
Let’s say a shopper is in the market for a pair of sunglasses for their child. They open their AI platform of choice and enter the prompt: “I’m looking for sunglasses for my kid that are stylish and won’t fall off.” The LLM replies with a series of questions like, “How old is your child?” and “Are they a boy or a girl, or do you prefer a gender-neutral style?”
2. Discovery
The answer engine generates a list of suggestions for products that match the shopper’s refined request. The shopper sees best-fit results from multiple stores. Some LLMs factor in the shopper’s previous searches and preferences. The shopper browses these options and finds a pair of sunglasses they like from a brand that has an agentic storefront.
3. Check-out
The shopper selects a product within the storefront. They click or tap a “Buy” button inside their conversation. They proceed as usual, providing their shipping and payment information, then check out without ever leaving the AI chat interface.
If a brand doesn’t have an agentic storefront, the shopper still sees products that are a good fit for a given query. But to buy, they have to go to the brand’s website to make their purchase.
4. Fulfillment
The store receives the agentic storefront order like they would any other. The store can see channel or referrer attribution in Shopify, and use it in segmentation .
5. Post-purchase updates
The shopper gets the same order updates as if they’d purchased directly from the store. But for customers who purchase products through an agentic storefront, post-purchase messages take on a more meaningful role to help build the customer relationship. This is because they may not have visited your website to buy.
For customers who purchased through AI, introduce them to your brand and mission in your post-purchase messaging. For example, share your founder’s story, describe how your products are made, or send educational materials on how to make the most of what they bought.
What is not supported for agentic storefronts?
For Shopify agentic storefronts, the following are currently not supported for agentic storefronts:
- Subscriptions
- Product bundles
- Digital products
- Customizable products
- Certain delivery methods, like local delivery, in-store pickup, and pickup points
While automatic discounts are supported, certain LLMs may not provide a discount code field as part of check-out, so individual discount codes may not work.
How can brands update their web presence for agentic shopping?
Agentic storefronts are one of the newest developments in agentic commerce, which means using AI agents to discover, compare, and buy products.
Optimizing your online content for AI shopping means building on your established SEO practices so that AI platforms cite your content. Here’s how:
Find out how your brand appears in AI platforms
Test prompts across answer engines. Try general queries about the types of products you sell to see if your brand is recommended at all. Analyze what types of queries surface your products and which don’t.
When your brand appears, note whether product information is accurate. Check whether product names and images display correctly, whether price and availability are up to date, and if products are showing up in the correct category. If, for example, you search for sunscreens and an LLM suggests your non-SPF moisturizers, something’s gone wrong.
Structure your product data for AI discoverability
AI platforms index product information just like search engines. Make sure your product pages are clear and scannable, load quickly, and are mentioned on high-quality websites.
On a technical level, make sure identifiers like Global Trade Item Numbers (GTINs) and Stock Keeping Units (SKUs), pricing, and availability data are current, so that search results are clean and accurate.
Share AI-optimized content on your website
Content formats like detailed FAQs, buying guides, comparison tables, “best of” lists, and product roundups are all useful to answer engines.
AI models look for digestible, concrete information about how products are used and how they fit into a broader context. Think, “which tents are best for alpine conditions” and “which are best for rainy marshlands.”
This type of educational content is also a signal that your brand is an authority in your space, which further increases your chances of discoverability in AI-generated answers. Use an AI marketing agent to quickly create and refresh content based on recent customer questions, common queries in your vertical, and customer reviews.
Collect detailed customer reviews and UGC
A top use case for AI shopping is summarizing a large number of detailed reviews, according to Klaviyo’s 2025 BFCM Forecast . AI can help shoppers quickly understand what people like and dislike about any given product, in a fraction of the time.
Solicit high-quality, in-depth customer reviews and user-generated content (UGC) , so shoppers can find valuable, relevant information.
Review depth is just as important as volume. “This sweater is warm, soft, doesn’t shrink in the wash, and hasn’t pilled even after months of wear” is much more useful than “Love this sweater!” Incentivize customers to answer specific questions when they review, either with discounts or by making it easier with a survey.
K:AI Customer Agent provides 24/7 shopping assistance, empowering customers to get fast answers to common questions, update their orders and subscriptions, get personalized product recommendations, and escalate issues to a human when necessary.
Ready to add an AI shopping assistant to your storefront? Sign up for Klaviyo today.