AGENTIC COMMERCE: HOW AI AGENTS WILL RESHAPE ONLINE SHOPPING

What is agentic commerce and why it matters

Agentic commerce is a shopping model in which an autonomous AI agent completes an entire purchase on behalf of the user. Instead of simply answering questions or suggesting products, it can research options, compare alternatives, make decisions based on the user's goals, and complete the checkout process. You provide the objective (“find a quiet, well-reviewed dishwasher under $700 and order it”), while the agent handles the intermediate steps on its own.

Why does this matter? Because it changes who your store is effectively serving. Traditional ecommerce is optimized to persuade people with visuals, page layouts, urgency messages, and promotions. An AI agent instead evaluates structured information, constraints, and objective criteria. As more purchasing decisions are delegated to software, merchants will need to optimize not only for human shoppers but also for autonomous systems.

And the stakes are high enough to justify acting early. McKinsey estimates that by 2030 the U.S. B2C retail market alone could see roughly $900 billion to $1 trillion in revenue orchestrated through agents, with global projections reaching $3 trillion to $5 trillion. Even at the lower end, that is too much demand to leave unread by machines. This broader shift is explored in our e-commerce trends 2026 article, where we explain the three forces reshaping online retail.

Agentic vs conversational vs recommendation: three distinct AI layers

These concepts are related, but they represent different levels of AI involvement in the buying journey.

An ecommerce recommendation engine is the most passive layer. It analyzes user behavior and suggests products that might be relevant, but the customer still has to browse, compare options, and make the final decision.

Conversational commerce adds interaction. The customer communicates with a chatbot or voice assistant, asks questions, receives guidance, and can even get personalized recommendations. However, the AI still waits for the user to decide and complete the purchase.

Agentic commerce goes one step further. The AI agent does not simply recommend or assist – it acts. Given a goal and a set of constraints, it researches products, evaluates alternatives, selects the best option, and completes the transaction on the user's behalf with minimal or no back-and-forth conversation.

The three layers build on one another rather than compete. Agentic systems rely on recommendation technologies to evaluate products and on conversational capabilities to understand user intent. If you want to explore these foundations in more detail, see how AI personalization, recommendations, and search work and how chat, voice, and messenger drive online sales. Understanding those layers helps explain why agentic commerce behaves differently.

Leading AI agent platforms in 2026

Agentic commerce is gaining momentum fast, and a handful of platforms are driving the conversation. OpenAI's ChatGPT Operator can browse the web and take actions like clicking and filling out forms. Perplexity's Comet is a browser-based agent built around research and comparison. Anthropic's Claude offers "computer use" capabilities that let it operate software much as a person would. Google's Gemini is adding similar agentic features across its ecosystem. In commerce specifically, Amazon's Rufus and Microsoft's Copilot are arguably ahead – both now handle checkout at scale. The capabilities overlap: these platforms browse, click, fill forms, and complete purchases.

The adoption numbers back this up. According to Deloitte, 95% of retailers have already implemented or plan to implement agentic AI – and 70% of customers are comfortable using agentic AI to make purchases on their behalf. Demand and supply are moving fast in the same direction.

How AI agents discover and compare products

A typical agent follows a predictable flow: it interprets the user's request, runs a web search, navigates to candidate sites, extracts product details, compares them against predefined criteria, and makes a decision. Each step is a place where your store either gets included or quietly dropped.

Two factors heavily influence whether a store stays visible. The first is structured data, which helps agents accurately understand product information, pricing, availability, and reviews. The second is site performance: agents evaluate many pages quickly, so fast, stable websites are more likely to stay in consideration. The same Core Web Vitals that improve human shopping experiences also help AI systems process information efficiently.

The machine-readable web: how merchants must prepare

If there is one practical message in this article, it is this: agents can only buy what they can read and trust. Preparing for agentic commerce is mostly about getting clean, well-structured fundamentals right. A handful of them matter most, and the table below explains what each one is and why it matters for your business.

The AI layers behind agentic commerce

An agent is only as good as the systems working underneath it. Before it can shop well on someone’s behalf, three things need to be running quietly in the background: AI ecommerce personalization that matches products to what a shopper actually wants, a conversational layer that can check in with the person when a decision needs human judgment, and reliable recommendations that narrow the options. Agents don’t replace these tools – they build on top of them.

The good news for business owners: money already invested in AI ecommerce isn't wasted when agents arrive – it becomes the ground they stand on. The cleaner your data and the more effective your personalization, the more confidently an agent can make decisions. You can see this kind of ecommerce work in the Modsen e-commerce portfolio.

Risks, compliance, and B2B considerations

None of this is risk-free, and a credible plan has to name the trade-offs. The main concerns fall into three areas: fraud, accountability, and compliance. Payment fraud is the most obvious challenge: if an agent uses a stored card, who is liable when something goes wrong? Deloitte reports that only 3% of retailers feel well prepared to address AI-enabled fraud – a sobering number given how fast agent traffic is growing. Beyond fraud, there are open questions around disputed agent-made purchases, brand impersonation, and data privacy when agents act without explicit user consent under regulations such as GDPR.

Fortunately, most of these risks are manageable with the right controls. Retailers can verify agent identities, require confirmation before purchases are completed, and log agent activity for accountability. The goal is not to block agents but to let them operate safely. This is where ecommerce automation becomes especially valuable, helping businesses apply the same safeguards consistently rather than relying on manual review.

These governance requirements become even more important in B2B environments. Agentic procurement fits naturally with recurring and rules-based purchasing, but business transactions must still comply with approval workflows, budget controls, and audit requirements. That is why automated ordering works best when agents are connected to ecommerce CRM and procurement systems, ensuring purchases remain both efficient and fully traceable.

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Conclusion

Agentic commerce turns shopping into something software can do on a person’s behalf, and it rewards merchants whose stores are easy for a machine to read, quick to load, and clearly trustworthy. The technology is still early, and the risks are real, but the businesses laying clean foundations in AI ecommerce today are the ones agents will choose tomorrow. You don’t need to rebuild everything at once – start with structured data, dependable product feeds, and an architecture that serves both people and machines.

Talk to the Modsen e-commerce engineering team about preparing your storefront for agentic commerce – from structured data and product feeds to a headless ecommerce foundation built for both customers and AI agents.