AI shopping agents are fundamentally changing the way people search for, compare, and buy products. Customers no longer have to click their way through traditional online stores step by step. Instead, they express their needs in natural language, and AI agents take care of the rest: they search for suitable offers, evaluate them, and, increasingly, complete the purchase themselves. For retailers and brands, this creates a new digital touchpoint that requires reliable product data, powerful interfaces, and a tailored marketing strategy.
In this article, you’ll learn:
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what distinguishes Agentic Commerce from traditional chatbots and conversational commerce,
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what technical foundations and data structures make a store agent-ready,
- how marketing, visibility, and specific use cases are changing in B2C and B2B commerce.
How your customers will shop in the future
Imagine a potential customer asks ChatGPT, “Show me the best running shoes under 120 euros for beginners.” Seconds later, she’s shown three suitable models—complete with reviews, size recommendations, and a “Buy Now” button right in the chat. With one click, she confirms her shipping address and payment, and the purchase is complete. She never even opened a traditional online store during this buying process—she simply chatted with ChatGPT.
This behavior is no longer a niche phenomenon: A recent analysis by Adobe Analytics shows that around 40% of consumers have already used generative AI like ChatGPT or Gemini for their online shopping, and more than half plan to specifically use such tools in the purchasing process this year. At the same time, traffic from generative AI sources to U.S. retail websites has risen by more than 1,000 % within a year and, in some months, by a multiple of that (up to around 4,700 %). A significant portion of the customer journey therefore already takes place in AI interfaces, rather than solely in traditional search engines or online stores (Adobe, 2025).
This is exactly what Agentic Commerce is: AI shopping agents handle research, comparison, and even checkout in e-commerce. They shift purchasing decisions from your usual touchpoints (e.g., Google search results or online stores) to new, agent-driven interfaces. Instead of users navigating through categories, filters, and product lists in online stores themselves, they articulate their needs in natural language and leave the rest to an AI agent, which can find and evaluate offers and complete purchases directly.
For e-commerce decision-makers, this means that visibility, conversion, and customer loyalty will no longer be determined solely by search engines, but by the “minds” of these AI agents. In this article, you’ll learn what lies behind Agentic Commerce, how it works technically, and what steps you should take today to ensure your store doesn’t get lost among AI recommendations tomorrow.
Agentic Commerce is more than just a smart chatbot
At first glance, Agentic Commerce sounds like “more of the same”: an AI that answers questions, recommends products, and guides customers through the purchasing process. The key difference, however, lies in the degree of autonomy with which these systems operate.
While traditional chatbots in conversational commerce primarily conduct dialogs, answer questions, and redirect users to existing online stores, agentic AI systems plan independent tasks across multiple steps and systems. They break down a goal into subtasks, call APIs from various retailers, evaluate options based on defined criteria (e.g., price, delivery time, reviews, return rate), and then trigger specific actions such as completing the purchase and processing payment.
In the context of agentic commerce, this means: A customer formulates a goal (“Find me beginner-friendly running shoes for under 120 euros”), and the AI agent takes care of the rest. It searches for suitable products across multiple stores, compares prices and delivery times, takes size recommendations and personal preferences into account, and can (within predefined budgets, limits, and approval rules) complete the purchase with a single click from the customer. The transaction is thus technicallyinitiated and executedby an autonomoussoftware agent via standardized interfaces, rather than step-by-step by the user in the browser.
For companies and their online stores, this shifts the role of AI: from an advisory front-end chat to an active market participant working behind the scenes.
AI agents act like digital shoppers who check your product data, prices, shipping terms, availability, and service levels in a machine-readable format and then decide whether your offer makes the shortlist or isn’t even displayed in the first place.
How to Make Your Store Agent-Ready
For AI agents to make informed purchasing decisions, they need something that is still missing in many online stores today: clean, structured, and complete product data.
They don’t work with your front end, but rather with catalog information such as titles, descriptions, attributes, prices, availability, images, brands, and IDs—ideally consistent across all channels.
How does your product data reach AI agents?
The better this data is maintained, the more easily agents can understand your product range, compare it with other offers, and make informed recommendations. Incomplete attributes, inconsistent prices, or unclear variant logic can result in your product either not making it onto the agents’ shortlist at all or performing poorly in comparisons.
In practical terms, this means:
- Make the best possible use of a PIM for your product data (depending on your product range)
- Define important required attributes
- Measure your data quality regularly
- Exportstructured data (e.g.,schema.org/JSON-LD) for products and offers
Modern platforms like Shopify and Shopware already provide a solid foundation for this. With its Catalog and corresponding APIs, Shopify offers a centralized, agent-ready view of products, including prices and inventory across all participating storefronts. Shopware 6 also offers clearly structured catalog data via its Product and Store APIs, which AI agents and AI product advisors can access—an important building block for making your product range agent-ready.
APIs as a Gateway to Your Store
AI agents don’t “click” through your pages; instead, they communicate with your commerce system via interfaces. They need APIs to search catalogs, check prices and availability, query shipping options, create shopping carts, and place orders. Without well-documented, stable, and secure APIs, your store remains virtually invisible to agents—even if your front end is perfectly optimized. API-first thus evolves from an IT architecture decision into a revenue-generating strategy. If your APIs are unreliable or provide outdated information, agents will be less likely to recommend your products because they can’t trust them.
Here, too, Shopify and Shopware are showing the way forward. With “Commerce for Agents,” Agentic Storefronts, and checkout integrations, Shopify explicitly opens its own APIs to AI agents, including a universal shopping cart and direct checkoutfrom within ChatGPT conversations. Shopware relies on a powerful admin and store API, along with initial agentic features in Shopware AI, which allow external assistants to access specific product, customer, and order data.
For you as a retailer, this means: If your platform already has strong API capabilities, you have a real head start in the race for agent visibility.
Your Roadmap to Greater Visibility
1. Assessment of Data and Interfaces
Before you plan new features, you need an honest assessment of your current setup. Systematically review your product data and interfaces so you can see in black and white just how “agent-ready” your store actually is today.
- Product data is complete and consistent across all channels
- Attributes, IDs, prices, inventory levels, policies, and reviews are clearly structured
- Excel lists and free-text fields have been replaced as data sources
- APIs for the catalog, shopping cart, checkout, and customer accounts are available
- All APIs are documented and operate reliably
2. Create an agent-friendly foundation
Based on the assessment, prioritize a few high-impact improvements. It’s crucial that external agents can reliably access your offerings and evaluate them in real time. You can often build on existing features of your platform, such as Shopify or Shopware.
- Required attributes are defined
- Product data has been cleaned and enriched
- Structured data (schema.org / JSON-LD) has been implemented
- Prices and inventory levels are synchronized across systems
- Catalog search, availability, shopping cart, shipping, payment, and order creation are accessible via API
3. Launch Agentic pilot projects
Once the database and APIs are in place, don’t get bogged down in the concept phase. Start with manageable pilot projects that have clearly measurable goals, and scale up based on the results.
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A proprietary shopping assistant based on your catalog and rules is being piloted
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Initial B2A scenarios with selected AI platforms or marketplaces have been tested
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Each pilot has a clear goal: conversion, revenue through agency channels, or reducing the burden on customer service
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Results have been evaluated, and the next steps—scaling or fine-tuning—have been determined
What Agentic Commerce Means for Your Marketing
With Agentic Commerce, you’re no longer just competing for clicks from people—you’re competing for spots on the AI agents’ recommendation lists. These systems are less likely to be swayed by your creative ads or out-of-the-box visuals, in the sense of the human attention economy. Instead, they evaluate the hard facts of your offers: data quality, prices, delivery times, return policies, reviews, and historical performance.
While traditional SEO and performance channels remain relevant, they now face competition: it’s also crucial whether your storeappears in the“Top 3” of an AI agent’s recommendation.
For marketing, this means: Campaigns must increasingly focus on building trust and generating signals for agents, not just for humans.
New Tasks for Your Marketing Teams
Marketing must now integrate even more closely with product management, data, and IT.
Your product data, policies, loyalty rules, reviews, and user-generated content are becoming marketing assets that must be structured in a way that agents can interpret them. Your teams need the ability to handle structured data storage, feeds, schema markup, and testing in AI/agent environments—in addition to traditional disciplines such as campaign management and content management.
Making Your Marketing Strategy Agent-Ready
For your marketing, Agent-Centric Commerce represents a twofold transformation.
First: SEO becomes Agentic SEO.
It’s not just about ranking on a Google results page, but about being selected by agents during the decision-making process. AI models weigh data completeness, attribute clarity, structured comparisons, sentiment from reviews, and pricing/promotional patterns. These are crystal-clear criteria you can use to make your decision—and they must be provided in exactly the same way so that LLMs can work with them.
Second: GEO (Generative Engine Optimization) comes into play. You optimize content and product information so that LLMs and generative search results correctly understand, cite, and recommend your brand—with a focus on structure, context, and semantic relevance rather than just keywords. You can find more information about GEO here: GEO versus SEO: The Rules of the Game for Visibility Are Changing.
In practical terms , this means: technical SEO best practices, structured product data, consistent brand messaging, and strong, credible content on the platforms from which models draw their information.
How a shopping assistant advises your customers in the store
An agent-based shopping assistant in your own store is a logical first step:
A customer describes their request in natural language (“I’m looking for a business sneaker under 150 euros that I can wear to the office”), and the assistant searches the product catalog, filters by style, price, size, availability, and reviews, and suggests a few suitable options. Instead of clicking through navigation menus and filters, users have a conversation, and the agent handles the research and comparison and can add products directly to the shopping cart or prepare the checkout.
For retailers, this means fewer drop-offs duringthe discovery phase, higher conversion rates, and better utilization of existing product data.
Agent-Optimized Marketplace and Platform Listings (B2C/B2B Marketplace)
On marketplaces and within platform ecosystems, the focus of optimization is also shifting from “visibility for humans” to “visibility for humans & agents.” Agent-based shopping assistants search marketplace catalogs, compare products based on price, delivery time, return rate, seller rating, and compliance signals, and use this information to generate shopping lists or shopping carts.
Retailers who maintain complete product data, establish clear policies, and offer competitive terms are thus preferred by agents, even if they aren’t necessarily the strongest brand in the end customer’s mind.
Digital purchasing agents for reorders (B2B)
Inthe B2B context as well, agents can act as digital buyers in the future: A procurement agent accesses customer-specific catalogs, contract terms, and budget approvals and handles routine processes such as “Reorder all consumables that will run low in the next four weeks.” It compiles shopping carts from preferred suppliers or marketplaces, applies framework agreement terms, checks budget limits, and submits orders or requests for quotes for final approval.
For vendors withB2B stores, this means that those who make their contract data, prices, and catalogs available in a format compatible with procurement agents will be included in these automated reorder processes.
How to Get Started with Agentic Commerce Now
Agentic Commerce is no longer a distant vision of the future. ChatGPT, Gemini, Perplexity, and other platforms are already integrating shopping features today.
AI agents are evolving from an experiment to a standard touchpoint in the near future of the customer journey. For you as a company or brand, this means: Those who lay the groundwork now can actively help shape how agents evaluate, present, and recommend your offerings. Those who wait risk no longer appearing in the agents’ recommendations.
The good news: You don’t have to start from scratch. By conducting a structured assessment of your data and APIs, making targeted optimizations to product information, and running initial pilot projects in your own store or on agent platforms, you can quickly achieve measurable progress.
This is exactly where SUNZINET supports you: from the Agentic Readiness Check and roadmap workshops to the implementation of your first shopping assistants or API integrations—on Shopify, Shopware, or your existing commerce platform.
Because if AI can find you, so can the people behind it.
