A 2025 survey found that a third of US customers planned to use artificial intelligence (AI) to assist with their holiday shopping. An AI shopping assistant can query catalogs, compare options against user-defined criteria, and even complete transactions without ever requiring a human to visit a storefront. Online retailers must fine-tune their storefronts for machine buyers as the practice of using these assistants becomes more popular.

Below, we’ll explore where agentic shoppers are gaining traction, how payments and catalog infrastructure can support machine-initiated transactions, and how to set up a commerce site for these systems.

Highlights

• Agentic commerce uses AI agents to execute purchases with only final approval from a human shopper.

• Retailers built for human shoppers can experience compatibility gaps with agentic ones.

• Clean catalog data, tokenized payment support, and checkouts that are accessible via application programming interfaces (APIs) are important for compatibility with agentic shoppers, regardless of what you sell.

What are AI shopping assistants?

AI shopping assistants are autonomous AI agents that can execute purchases on behalf of a human user. An agent can browse product catalogs, compare specifications and prices across multiple sources, apply user preferences (e.g., brand, sustainability criteria, delivery windows), and complete a transaction once the user gives final approval.

How does agentic commerce work?

An agentic commerce transaction begins with human instructions. The AI agent then handles the rest before it loops back to the user to approve the final payment. Once the user accepts, the AI agent completes the purchase.

Here are the steps:

• The user gives the agent a goal and constraints: A sample instruction might read, “Buy me a replacement filter for my air purifier—same brand if possible, under $40, delivered by Thursday.” Those constraints then govern the agent’s decisions.

• The agent queries product sources: The agent finds product options by scraping structured data from a page, or querying a retailer’s product API or an AI-enabled catalog interface. Cleaner data leads to faster, more accurate matching; incomplete or inconsistent catalog data will cause the agent to avoid that retailer.

• The agent evaluates the options against the constraints: Price, availability, delivery timeline, seller reputation, and return policy are all weighed programmatically against the user’s parameters.

• The agent loops a human in for approval: The agent sends what it found to the human shopper, who either approves or rejects the purchase.

• The agent initiates the transaction: If the human shopper approves, the agent completes the payment without a human at checkout. This requires tokenized payment credentials, delegated authorization, or wallet-level integrations.

• The agent reports back to the user: The user gets a notification with the order number, expected delivery date, and receipt. This whole loop might complete in seconds.

Which retail categories are common for agentic commerce?

Not every retail category is well suited to agentic buying at the moment. Scenarios where the purchases are straightforward and repeatable are clear early use cases.

Here are categories where agentic buying makes sense:

• Consumables and replenishment: When the user knows exactly what they want and needs to buy it again, an AI agent is an easy choice. This category includes household supplies, printer ink, pet food, coffee, and anything else that gets refilled.

• B2B procurement: This could become the most commercially consequential near-term application for agentic commerce. A procurement agent that’s authorized to spend up to a certain amount per order, within a preferred vendor list, can handle routine supply orders end to end.

• Price-sensitive electronics and commodity tech: Agents can monitor pricing across sources and execute when a product reaches a target price. Businesses that sell in this space need a pricing strategy that accounts for algorithmic buyers that will detect and act on discounts more quickly than humans can.

• Event-triggered purchases: “Buy X when Y happens” is a natural instruction for an agent. Agents can buy immediately when a product goes back in stock, a delivery is confirmed, or a price drops below a threshold.

What are some benefits and risks for retailers as agentic commerce grows?

Agents are ruthlessly efficient. Having them as customers comes with both pros and cons.

Benefits

• Higher conversion on qualified traffic: Agentic buyers don’t abandon carts just because they got distracted or confused. If your product is the best match for the agent’s criteria and your infrastructure supports machine-initiated transactions, conversion is very likely.

• Automated repeat purchasing: Retention doesn’t depend on whether the customer remembers to come back. Instead, the agent comes back on schedule, which turns replenishment categories into reliable recurring revenue without any remarketing spending.

• Access to B2B procurement channels: If a procurement agent is authorized to buy from any vendor that meets certain criteria and your product and API meet them, you’ll be under consideration without needing a sales call.

Risks

• Brutal price transparency: Agents compare prices in real time. If a competitor is consistently cheaper on a commodity product, agents will move there without considering brand loyalty.

• A different attack surface: High-volume, machine-initiated transactions invite new attack patterns. Fraudulent actors can deploy agents to test stolen credentials or probe checkout logic at scale, which means your fraud detection needs to account for speed and pattern anomalies that don’t resemble human shopping behavior.

• Infrastructure dependency: When agents account for a large share of your transaction volume, API downtime or breaking changes have a much larger impact than they would with human traffic.

• Thinner customer relationships: When an agent buys on someone’s behalf, that’s a transaction rather than an interaction. Your customer is less likely to understand your brand, examine your other offerings, or tell a friend.

How can retailers set up their ecommerce sites for agentic commerce systems?

Storefronts are built for humans, and agents don’t shop the way humans do. A retailer’s ability to attract both audiences determines whether it is chosen or skipped.

Here’s what an AI shopping assistant is looking for:

• Structured data: Agents don’t respond to home page banners, lifestyle photography, or carefully sequenced product storytelling. Instead, they parse structured data, follow APIs, and evaluate price-to-specification ratios. If agents can’t reliably query your catalog for accurate pricing, real-time inventory, and structured specifications, they’ll turn elsewhere.

• Simple checkout: Agents might not tolerate multistep checkout flows, CAPTCHA verification, or account creation requirements. If your purchase flow can’t be completed via an API call, the agent might route to a competitor.

• Tokenized payment support: Your payment provider needs to support stored credentials and delegated authorization in addition to card-present and human-initiated flows. The Stripe API lets businesses accept machine payments.

• Webhook-based order confirmation: Agents need machine-readable confirmation that a transaction has been completed.

Ecommerce businesses will need to adjust their models for machine buyers in a few other ways. Brand loyalty will become less important. While human shoppers factor in familiarity and trust in ways that can override a price gap, agents weigh only the criteria given by their users. Capacity planning will also need to change. One person buys one thing at a time, while one agent can initiate thousands of transactions in minutes as it serves thousands of users simultaneously.

How Stripe Payments can help

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