AI systems in e-commerce have long since moved beyond simply providing advice; they are increasingly taking over individual steps of the purchasing process. Agentic Commerce has gained significant attention and practical relevance in recent months. At the same time, the hype is growing: “The traditional online store is dying,” “Personalization is becoming obsolete.” For store owners, this is unsettling and often not very helpful. This article explains what you, as an online retailer, need to know right now. It explains where Agentic Commerce really works, what that means specifically for your store, and what role personalization plays in this.
Here's what you can expect to find in this blog article:
What is Agentic Commerce?
A Definition with a Gray Area
Agentic Commerce vs. Conversational Commerce
What Sets Agent-Based Systems Apart
How does Agentic Commerce work?
From Proposal to Action
The Role of Data and Interfaces
What opportunities does Agentic Commerce offer customers and merchants?
Benefits for Customers
Opportunities for Merchants
Where Agentic Commerce Works—and Where It Doesn't
Factor 1: Known vs. Unknown Preferences
Factor 2: Repeat Purchase vs. Discovery
Factor 3: Involvement in the Purchase
Use Cases: Where Agentic Commerce Is a Natural Fit Today
What Agentic Commerce Means for Your Store
If you’re a commodity store
If you run an experience-based store
What Agentic Commerce Means for Personalization in the Online Store
Agents and online stores rely on different signals
Personalization in the online store remains a key strength
Relevance drives not only conversions but also customer loyalty
The Challenges of Agentic Commerce
How Retailers Can Prepare for Agentic Commerce Now
Making the Store Accessible to Agents
Enhancing the shopping experience for people
Conclusion: Agentic Commerce – Between Automation and the Shopping Experience
Key Points at a Glance
- Agentic Commerce refers to shopping with AI agents. They can handle parts of the purchasing process on behalf of customers—from research and comparison to selection and, depending on authorization, completing the purchase.
- Not all purchases are automated to the same extent. Routine purchases with clear preferences are better suited to agent-based processes. For purchases that involve discovery, consultation, or personal decision-making, the store remains particularly relevant.
- Commodity stores and experience-based stores are affected differently. Standardized product lines should be technically accessible and reliably comparable. Experience-based stores should build on their strengths in customer advice, brand experience, and customer loyalty.
- Personalization remains a key competitive advantage. Relevant recommendations, improved navigation, and a more personalized shopping experience can help build long-term customer loyalty to your store.
- Technical infrastructure and the shopping experience should be considered together. Comprehensive product and offer data provide the foundation for discoverability and comparability. At the same time, retailers should strengthen their direct contact with customers and the added value of their own stores.
Agentic Commerce describes an approach in whichAI agents independently research, compare, decide, and make purchases on behalf of consumers or businesses, often without direct human intervention at every single step.
The term is not yet used consistently across the board. Some articles already include AI-powered shopping assistants or chat-based advice—that is, systems that lean more toward conversational commerce or guided selling. Others use the term “Agentic Commerce” to refer only to scenarios in which an agent actually acts autonomously and executes transactions.
For the purposes of this article, therefore, a narrower definition makes sense: the term refers here to systems that autonomously handle at least parts of the purchasing process, from research and comparison to execution.
In practice, the lines are blurred. On one hand, there is conversational commerce, in which AI—for example, in the form of an AI shopping assistant —supports product selection and purchasing decisions, but the human remains actively involved in the purchasing process. On the other hand, there is agentic commerce in the narrower sense—that is, scenarios in which an agent independently researches, makes decisions, and executes transactions. Many current solutions still fall somewhere in between.
An AI shopping assistant provides conversational guidance, allowing users to actively ask questions and participate in the purchasing process. (Source: Entirely Demo Shop illustration)
This is also evident in many real-world examples currently being discussed under the label “Agentic Commerce.” Often, these examples still focus primarily onAI-powered product search, comparison, and purchase guidance—in other words, conversational commerce or shopping assistance rather than fully autonomous purchasing processes.
IBM defines AI agents as systems that perform tasks autonomously by designing workflows using available tools. Unlike simple rule-based chatbots, modern agents can reason, plan, and act across systems and platforms.¹
Three features fundamentally distinguish these systems from earlier AI applications in retail: They operate autonomously, meaning they do not require human confirmation at every step. They respond dynamically to changes in their environment, such as when a product suddenly goes out of stock or a competitor lowers its price. And they are interoperable: Through open APIs and standardized interfaces, they can communicate with various systems and platforms without being tied to a single provider.
In simple terms, an agent-based purchasing process can be broken down into several steps:
The difference from a traditional chatbot or a simple recommendation algorithm lies not only in speed, but above all in the agent’ s ability to take action: The agent doesn’t just make suggestions, but can coordinate multiple steps and—depending on authorization—also carry them out.
Various technologies work together behind the scenes: Language models help understand the customer’s inquiry and process relevant information. Agent software handles the planning and coordination of individual tasks. Through data feeds, APIs, or other interfaces, the agent can access product, merchant, and transaction information.
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Let’s return to the example of the fragrance-free laundry detergent: In order for an agent to make an appropriate selection, they need, among other things, information on product features such as “fragrance-free” and “suitable for sensitive skin,” a current price, availability, and a reliable estimate of the delivery time. If such information is missing, contradictory, or out of date, the agent may not be able to compare offers accurately. The delivery estimate may also depend on the delivery location, the time of order, and the available shipping options. This information should be complete, up-to-date, and clearly structured.
The technical solutions that are relevant here depend on the e-commerce platform, the platform itself, and the specific use case. Structured data, data feeds, APIs, or other standardized interfaces may play a role. Depending on the use case, CRM, ERP, warehouse, shipping, or customer service systems may also be involved, in addition to e-commerce and payment systems.
Various approaches to communication between AI systems, online stores, payment providers, and other commerce systems are currently emerging in the market. These include, among others, various protocols and standards such as ACP (Agentic Commerce Protocol), UCP (Universal Commerce Protocol), and MCP (Model Context Protocol).
Classification
Not every process currently referred to as “Agentic Commerce” is already fully autonomous. The term encompasses varying degrees of automation: Depending on the application, AI systems handle research, comparison, or purchase preparation. Whether the customer confirms the purchase themselves or the agent is authorized to initiate the purchase within predefined rules depends on the customer’s authorization, as well as on the platform, product category, country, and payment infrastructure.
Agentic Commerce can both simplify the shopping experience for customers and create new opportunities for retailers. However, the specific benefits depend on the shopping occasion, the product assortment, and the degree of automation possible.
Important: These benefits do not arise automatically from the use of an AI agent. They require, among other things, up-to-date and complete product and offer data, clear rules, and appropriate technical access.
Not every one of these opportunities is equally effective in every purchasing context. This is precisely the key point that many hype-driven articles overlook: Agentic Commerce does not work the same way everywhere. What matters most is how a purchase is experienced and decided upon.
In many current classifications, agentic commerce is defined very broadly and is sometimes described as encompassing the entire customer journey. However, if we look at specific use cases, a clearer picture emerges: agentic behavior is particularly useful in situations where purchases are recurring, standardized, or clearly specified —such as for reorders, price monitoring, or B2B procurement. This is precisely why it is not the entire e-commerce sector that is becoming agentic, but primarily the efficiency-driven parts of it.
A key difference is that agentic commerce is primarily focused on efficiently resolving a specific purchasing need. The emphasis is not on browsing, but on quickly achieving a goal. For many purchases, this is an advantage. For others, however, the opposite is valuable—namely , discovering, comparing, and finding inspiration. And that is precisely where the store remains relevant as a space for experiences.
Three factors determine when it makes sense for users to use an AI agent:
Does anyone always buy the same brand of laundry detergent in the same size? If so, an agent can handle that without thinking. The preference is known, and the decision is trivial.
Is someone looking for a new perfume that fits their changed lifestyle? In that case, their preference is unknown or not at all consistent. Here, the person remains involved in the decision.
Routine purchases such as office supplies, consumables, or dietary supplements are ideal candidates for automation. The added value lies in convenience. Many users do not want to handle these types of purchases themselves. This is also reflected in the current discourse: An article on OMR states that AI agents primarily satisfy the need for convenience and time savings, especially when it comes to everyday products and recurring purchases.²
New purchases, where inspiration, comparison, and the discovery of something unknown are part of the experience, often cannot be easily delegated entirely because the experience itself is part of the value.
People often don't want to actively make decisions about low-involvement purchases (commodity products—that is, standardized and interchangeable products—as well as inexpensive consumer goods). Automation is welcome in this context.
High-involvement purchases (fashion, furniture, gifts, lifestyle products) are emotionally charged. Browsing, comparing, discovering—that’s the experience. An agent cannot easily replace this experience because the value lies precisely in the discovery, comparison, and personal decision-making—and it isn’t a problem that needs to be solved.
The less familiar the preference, the more the purchase is driven by discovery, and the higher the level of involvement, the more important the store remains as a central point of contact (Source: Author's own illustration)
The key factor, then, is whether a purchase should be completed as efficiently as possible or consciously experienced. The more the focus is on preference, repeat purchase potential, and functional utility, the more appropriate it is to use an agent. The more discovery, inspiration, and emotion shape the purchase, the more important the store remains as a space for experience.
The key takeaway: Agentic Commerce automates efficiency—not emotion.
To ensure this doesn't remain just a theory, let's take a look at a few areas where agentic commerce seems particularly plausible today or is already beginning to take shape:
The examples show that Agentic Commerce is relevant not only for traditional online stores, but also for areas such as subscriptions, travel, and ticketing—in other words, wherever clearly defined, recurring, or process-oriented decisions are made.
The extent to which Agentic Commerce will transform your store depends primarily on one question: “Do my customers shop with me because it’s convenient, or because it’s fun?” The answer to this question determines how much Agentic Commerce will impact your business.
Anyone who sells consumer goods, standardized products, or office supplies faces a structural challenge: In agentic commerce, preference is given to retailers whose offerings are reliably discoverable, understandable, comparable, and accessible for transactions by agents. Listings with complete, consistent, and machine-readable information, along with clean product data, are better positioned to be found and compared by agents.
Specifically: If your store isn’t “agent-readable”—that is, if it doesn’t offer structured product data or open interfaces—it will become increasingly difficult to be included in agent-supported purchasing processes. Not because the customer doesn’t like you, but because the agent can’t find you.
In addition, AI assistants and LLM-based applications are playing an increasingly important role in the discovery and preselection phases. They help understand user intent and direct users to the right retailers. This is precisely why clean product data, clear categorization, and technical accessibility are so important. The actual moment of purchase and everything that builds trust—from checkout and loyalty to identity and risk management—remains closely tied to the retailer.
Areas of Focus for Commodity Shops:
Fashion, lifestyle, home decor, jewelry, gifts—in these categories, agentic commerce tends to have less of an influence on the purchasing process. When it comes to a personal purchase, such as the perfect birthday gift for a best friend, the decision itself remains an important part of the shopping experience for many people.
That doesn’t mean, however, that experiential stores can ignore this shift. Those who use agents to automate more routine purchases will have less time and patience overall for mediocre shopping experiences. On top of that, discovery and selection may increasingly take place through LLMs or AI assistants in the future. In this context, the term “zero-click commerce” comes up time and again. It describes the idea that users are going through fewer and fewer traditional shopping steps on their own. It remains to be seen just how far this will go. One thing is clear, however: even inspired purchases are increasingly starting with a question posed to an AI assistant. Clean product data and technical interoperability therefore remain relevant for experience-driven stores as well. Those who can’t be found there will lose potential visitors before they even enter the store.
But even if Discovery may increasingly take place in external interfaces in the future, the online store will not disappear from the customer journey. Recent analyses, such as those by Adyen, show that these systems can serve as an additional channel for retailers to reach users with a high intent to purchase. At the same time, neutral chat interfaces do not automatically evoke the sense of familiarity, guidance, and brand identity that a store or brand website can convey. It is precisely this emotional aspect of the shopping experience that should not be underestimated, and it is a key reason why the store itself remains relevant even in the age of agentic commerce.³ External AI systems can generate attention and redirect users. However, trust, guidance, brand identity, and customer loyalty continue to be built primarily through direct contact with the retailer. For you as a retailer, this means above all that the online store must be compelling enough as an experiential space to justify visits—not just as a transactional channel. Personalization and inspiration are becoming more important, not less.
Areas of Focus for Experience Shops:
Both types of stores require a solid technical foundation. However, their priorities differ: Commodity stores should focus primarily on improving their transactional capabilities, while experience-driven stores should focus on the quality of search, digital guidance, inspiration, and customer loyalty.
| Commodity Store | Adventure Shop | |
|---|---|---|
| Impact | Direct: Agents can prepare or initiate standardized purchases | Indirectly: Discovery and selection may shift in part to external interfaces, and customers' attention and patience may decline |
| Role of the Store | An accessible and reliable transaction channel | A Space for Experiences, Counseling, and Relationships |
| Success Factor | Product Information, Availability, and Technical Accessibility | Inspiration, Emotion, and Trust |
| Key Areas of Action | Product Data, Interfaces, and Reorder Processes | Search, Advice, Inspiration, Customer Loyalty |
Agentic Commerce does not make personalization obsolete—quite the contrary. The more product search, preselection, and routine purchases become partially automated, the more important the question becomes of where true relevance lies. And this is precisely where your own store remains central.
One reason why a company’s own online store remains relevant for personalization lies in the differences in the data sets used by agents and online stores. Both systems can work with preferences. However, which preferences they take into account and how long they retain that information depends on what data they are allowed to access and the context in which they are used.
For example, an external shopping agent processes the requirements a user specifies directly, as well as any additional preferences on file: “I’m looking for laundry detergent—in powder form, fragrance-free, and from Brand X.” A store can also incorporate its own interaction and transaction data —such as search queries, clicks, shopping carts, purchases, abandoned carts, and repeat purchases. This can provide further insights into interests and behavioral patterns, provided this data is available and can be used in compliance with data protection regulations. As a result, store personalization can develop an additional strength: It not only takes into account the currently stated purchasing goal but can also incorporate the historical context of the relationship between the customer and the store.
That is precisely why store personalization is not a thing of the past, but rather a distinct advantage. It not only generates more relevant product recommendations —it also provides guidance, accelerates discovery, and makes the shopping experience cohesive and personalized. Especially in situations where people want to browse, compare, and make their own decisions, relevance isn’t achieved through efficiency alone, but through the right timing, context, and a personalized approach.
| Agent-based systems can take into account | Stores can take into account |
|---|---|
| Current Requirements
Preferences Saved by the User Context of Direct Interaction Preferences stored long-term, depending on the system |
Searches and Clicks in Your Own Store
Shopping Carts, Purchases, and Repeat Purchases Abandoned Cart and Previous Shop Contacts Behavior within One's Own Customer Journey |
Personalization doesn't just have an impact at the moment of purchase. It is a key tool for re-engaging customers with relevant recommendations, personalized emails, or content that builds on their past interests, and for encouraging them to return.
If product search and pre-selection are partially shifted to external AI interfaces, the direct relationship between the customer and the retailer becomes even more valuable. The retailer’s own store can link this to the context of previous interactions with the brand’s world, advice, and relevant services. External agents can complement the customer relationship—but the responsibility for the brand experience, trust, and long-term loyalty remains with the retailer.
Agentic Commerce brings not only opportunities but also real challenges:
Agentic Commerce is no longer a distant future scenario, but it’s also no reason to panic. For retailers, the first step is to review their own fundamentals and keep an eye on relevant developments. Two questions are crucial here: Can external systems reliably understand and find their offerings? And are there still good reasons for customers to visit their store directly?Both areas of focus are relevant for every store—the priority simply shifts depending on the product range and positioning.
Agentic Commerce is just one component of the broader e-commerce landscape—we’ve summarized other trends that will shape online retail through 2030 in our article on the future of e-commerce.
Agentic Commerce will transform e-commerce, but not every purchasing process in the same way. Routine purchases and efficiency-driven decisions are more easily automated, while inspiration, trust, brand experience, and personal relevance will continue to be key reasons for visiting a physical store. For you as a retailer, this means you should make your product data and systems interoperable while simultaneously building on the strengths that external agents cannot automatically replace—a relevant, inspiring, and trustworthy shopping experience. Personalization helps turn individual interactions into long-term customer relationships.
Sources: ¹ IBM, 2026, ² OMR, 2025, ³ Adyen, 2026
In traditional e-commerce, the customer largely navigates the purchasing process on their own: they search for products, compare offers, make a decision, and complete the purchase. In Agentic Commerce, an AI agent takes over one or more of these steps on the customer’s behalf—from research and comparison to purchase preparation or the transaction itself. The key difference lies in who makes the decision and how much autonomy is delegated to an AI system. The extent of responsibility the agent assumes depends on the specific authorization and the particular use case.
Conversational Commerce primarily describes dialogue-based interaction with customers, such as via a chatbot or an AI shopping assistant. The system provides advice and recommendations, while the customer typically makes active decisions and takes action. Agentic Commerce goes a step further: An agent can independently process requests, coordinate multiple steps, and even carry out actions within defined rules. In practice, the boundaries between the two approaches are still somewhat blurred at this time, and many solutions fall somewhere in between.
The customer first specifies a goal, such as searching for a particular product at a set price and with a specific delivery date. The agent interprets the requirements, searches through available product and retailer data, and compares suitable offers. The agent can then create a selection or a shopping cart. Depending on authorization, the customer confirms the purchase, or the agent initiates it within defined rules. The relevant retailer, payment, and logistics systems then process the order.
Agentic Commerce is particularly well-suited for recurring, standardized, and relatively low-risk purchases where preferences and requirements are clearly defined. These include, for example, reorders, office supplies, or other consumables. Full automation is less obvious for purchases that are heavily influenced by inspiration, personal advice, discovery, or emotional decisions—such as gifts, fashion, or home furnishings. In these cases, an agent can provide support, but the personal decision ultimately rests more with the customer.
Agentic Commerce primarily transforms those parts of the sales process characterized by routine, comparison, and efficiency. Product searches, price comparisons, reorders, and certain procurement processes can be partially handled by AI agents. For retailers, therefore, complete product data, up-to-date prices and availability, and technical accessibility are becoming more important. At the same time, Agentic Commerce does not automatically replace the traditional online store. For purchases driven by inspiration, advice, trust, or brand experience, direct contact between the customer and the retailer remains crucial. Traditional sales models are not being replaced but rather supplemented by a new channel—one that automates certain purchasing processes without replacing the human shopping experience.
First and foremost, complete, up-to-date, and clearly structured product and offer data are crucial. This includes, among other things, product features, variants, prices, availability, delivery times, and shipping and return policies. Depending on the use case, data feeds, APIs, or other interfaces may be relevant. In addition to online store and payment systems, CRM, ERP, warehouse, shipping, or customer service systems may also be involved. The specific solution that makes the most sense depends on the online store platform, business model, and sales channel.
For SMEs, Agentic Commerce is particularly relevant in two areas: on the buyer side, where automated procurement processes can save time and money, and on the retailer side, where technical interoperability—that is, clean product data and open interfaces—determines whether an online store will even be found by agents. This is particularly important for smaller retailers, who lack the reach of large platforms, as it provides a crucial foundation for remaining visible in a product search landscape that is increasingly organized by agents.
No, on the contrary. Precisely because routine purchases and initial product searches can, in some cases, be shifted to external AI interfaces, the quality of the experience within the retailer’s own store becomes even more important. Agentic Commerce can simplify product searches and certain routine purchases, but it does not automatically replace the relevance and guidance that a personalized shopping experience can provide. Depending on consent levels and available data, online stores can link the context of past interactions with current interests. Personalized search results, recommendations, content, and emails can thus support not only the purchase decision but also repeat visits and customer loyalty.
Yes. In the B2B sector, many of the conditions for agent-based processes are met: orders are repetitive, products and requirements are often standardized, and procurement rules can be clearly defined. Agents can, for example, compare quotes, take approved suppliers into account, prepare orders, or trigger reorders. For more complex or cost-intensive decisions, approvals and oversight by decision-makers remain particularly important.
Among the most important challenges are data protection, security, authorization, and the issue of accountability for incorrect decisions. Retailers must also ensure that product data, prices, availability, and shipping and return policies are complete, up-to-date, and unambiguous. And the more discovery takes place via external platforms, the more important it becomes for you, as a retailer, to maintain and actively strengthen your direct relationship with your customers.
Agentic Commerce is evolving step by step. Conversational commerce already offers retailers the opportunity to support customers in their purchasing decisions through dialogue-based advice, as well as relevant recommendations and information.
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