The retail industry is about to lose one of its oldest assumptions: that the shopper at checkout is definitely a human.

30 to 45 percent of U.S. consumers already use generative AI for product research and comparison, and that reliance will inevitably become more pronounced at checkout.

Agentic commerce is beginning to find its way into more consumers’ buying journeys as they look for new ways to shop.

If this new way of shopping maintains its pace, agentic shoppers could make up $190 billion to $385 billion in U.S. ecommerce spending by 2030.

AI agents aren’t only an emerging trend, they are becoming a new class of customer in the commerce ecosystem. But retailers' platforms and websites were not built for this kind of machine-led activity.

There are new pressures building on merchants to rethink and redesign their systems to support autonomous agents and avoid misclassifying legitimate traffic as risky when humans become more hands-off in their buying journeys.

Besides the challenge of becoming discoverable by AI agents, retailers need to be able to verify who is making transactions at checkout when the “shopper” is actually a machine.

That requires understanding which agents are authorized, which ones are malicious, and which ones represent real, valuable customers.

AI agents break the traditional trust model online

The status quo of online retail is being disrupted by AI agents, not because they introduce fraud directly, but because they break the signals merchants have relied on to measure trust for years.

Protocols and identity layers look increasingly different as agents operate in ways that can make them look like suspicious automation under today’s fraud rules. As agents make transactions using APIs rather than typical browsing flows, behavioral analytics loses its predictive power.

In many cases, the usual browsing journey that these brands have used to infer trust simply won’t exist. Retailers can’t assume that the agent is acting on behalf of a legitimate human without proof, so the question shifts from: “Is this user real?” to “Is this agent authorized to act for this user right now?”

The data already points to why this matters: By the end of 2025, online orders driven by LLM referrals were up more than 1,000% year over year. Even so, purchases executed by bots still make up fewer than 1% of all orders.

This is more than a shift in volume. The models that have been trained on human behavior patterns and to recognize bots as bad traffic now struggle when the “user” is a bot with no history and no trusted profile. The data gap creates a dual risk, more fraudulent activities slipping through, and more legitimate orders being declined.

The infrastructure behind agent-safe commerce

Retailers need to start treating AI agents as a new kind of digital customer in their trust systems. This requires an architecture that can authenticate which AI platform or agent is initiating a transaction, rather than treating all machine-driven interactions as anonymous bot traffic.

Ecommerce teams need to focus on providing machine-friendly commerce data with details like product pricing, in-store availability, shipping rules, and return policies that are well-structured, so agents can easily interpret them.

More importantly, they need to distinguish between three categories of activity, malicious automation, authorized agent-driven transactions, and blended human-agent behavior. And they need a way to instantly differentiate between automated threats and AI agents buying on behalf of valuable customers.

It’s a common misconception that the biggest threat retailers are facing is fraud, when the greatest risk is rejecting legitimate orders. What we are seeing now is retailers accidentally blocking agent traffic because it closely resembles typical bot traffic, which means they are losing visibility into how they are being recommended and selected and ultimately into transactions themselves.

Retailers need better classification systems that can separate hostile automation from authorized intent. This requires a more agent-ready commerce stack in five key areas:

Audit the stack for agent readiness: review product data, API accessibility, and machine-readable content to identify where trust breaks across the buying journey

Verify the agent behind the transaction: confirm the identity of the platform or service initiating the order (e.g. ChatGPT, Claude, etc.)

Prove the shopper’s permission: ensure the agent is acting with explicit authorization with controls around limits and categories.

Modernize fraud models for machine-led behavior: optimize classification accuracy, so legit agent-assisted orders aren’t treated like fraud.

Extend trust controls beyond checkout: prepare for agent-driven returns, exchanges, order edits, or support requests.

If retailers implement these steps, they are moving in the right direction to redesign the shopping experience and rebuild the infrastructure beneath it, so they can capture and not block agent-driven demand.

Machine-led commerce is on the horizon

For now, retailers and ecommerce merchants have time to adjust their strategies before agentic commerce enters a mature stage. The shift will start in narrower, repeatable purchase categories, but as the adoption grows, a competitive gap will emerge between retailers that prepared well, and those that didn’t.

To gain that advantage, online brands that modernize their identity, authorization, and risk infrastructure now will be in a better position to support machine-led transactions without adding any friction for the customer behind them.

The retailers that get this right will reduce fraud while capturing a new class of customers. Because even if shopping is done by machines, trust will still need to begin and end with the human customer.

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