AI Agents in Ecommerce: What Retail and B2B Businesses Should Test Before Launch

Photo: Freek Wolsink / Pexels

A customer finds a product on your website and asks two questions.

“Will this fit the space I have? And can you deliver it before Friday?”

Your AI assistant replies in seconds. The answer is clear, helpful and confident.

But has it checked the right product dimensions? Does it know the delivery postcode? Is Friday a date it can actually promise?

Those details can decide whether the customer buys, whether the order can be fulfilled and whether your team has to fix a problem later.

This is where evaluating an ecommerce AI agent becomes useful. You need evidence that its answers and actions hold up through the buying journey.

Let's look at two illustrative situations: a retail customer buying furniture and a business buyer ordering equipment. Neither example describes a client deployment.

Start with the decision your customer needs to make

A furniture shopper is comparing two desks for a small home office. They want enough space for their equipment, a finish that suits the room and delivery before they start a new job.

An assistant could help them compare the published dimensions, explain the available finishes and find the relevant delivery information. It could also ask how much space they have before recommending a product.

That is a useful scope for an initial implementation.

The business should decide what counts as a successful interaction. Perhaps the customer finds a suitable product with accurate information. Perhaps a question that needs a specialist reaches the right person with enough context to answer it.

Counting chat messages tells you very little about either outcome.

Also ask whether a simpler website improvement would solve the problem. If customers cannot find a delivery policy because the link is buried, making it visible may be the most useful first step.

Product knowledge needs a dependable source

Return to the shopper comparing desks. One product page gives the overall width, but says nothing about the space between the legs.

The assistant should explain that the measurement is unavailable and offer a way to confirm it. Guessing from the overall width could lead to a purchase that does not work for the customer.

For this workflow, identify which source owns each fact. Product specifications might come from the catalogue. Assembly instructions might come from approved documents. Current stock might come from the commerce platform or inventory system.

Give someone responsibility for correcting those sources when they disagree. An assistant connected to three conflicting descriptions still needs a rule for which one to trust.

Price and availability also need a freshness requirement. A saved copy of a product page may help explain the product, but it should not be treated as confirmation that a particular variant is available now.

Where a current check is unavailable, the answer should make that limitation clear.

Shipping questions require more than a policy summary

“Do you deliver to Ireland?” and “Can this desk arrive in Dublin by Friday?” require different information.

The first question may be answered from an approved delivery policy. The second may depend on the product, stock location, destination, order time and delivery service.

Define which of those details your assistant can check. If it only has access to the published policy, it can explain the usual delivery window and direct the customer to checkout or a person for confirmation.

A useful shipping test therefore checks more than whether the assistant finds the policy. It checks whether it recognises what the customer is asking it to promise.

B2B buying adds account rules to the conversation

Now consider a buyer ordering 80 monitors for several offices.

They need compatible models, a delivery schedule and a price for their organisation. They may also have agreed payment terms or a minimum order quantity.

These are ordinary commerce requirements. For example, Shopify's B2B catalogue documentation describes catalogue pricing, quantity rules and volume pricing. An assistant needs to work within the rules configured for the particular business.

The buyer's identity matters before private account information is retrieved. Typing a company name into a chat should not be enough to access its negotiated prices.

For a quotation request, the assistant could collect the required models, quantities, delivery locations and timing. It should ask the buyer to confirm those details before sending them to sales.

A submitted enquiry should remain an enquiry. It should not become an approved price, reserved stock or a confirmed order unless the authorised process actually completes those steps.

Decide what the agent is allowed to do

Reading a product specification and changing an order carry different consequences.

For an initial release, you might allow product questions and confirmed enquiry submissions while keeping discounts, refunds and order changes with your team.

Write down those boundaries before connecting the assistant to business systems. Give each connection only the access needed for the agreed task.

Make the handover useful too. If a person needs to take over, the enquiry should contain the relevant product, the customer's question and the details already provided. The customer should know whether someone will reply and through which channel.

An automated message saying “our team will contact you” is only useful when a team actually receives the request.

Turn these situations into evaluations

An evaluation is a repeatable test with an expected result. It gives the team a shared way to judge whether an agent handled a situation correctly.

Anthropic's guidance on agent evaluations distinguishes the conversation from the outcome in the connected system. That distinction matters when an assistant claims to have completed a task. The guidance also recommends repeated trials because results can vary.

For an ecommerce pilot, we suggest starting with the following scenarios and adapting the expected results to your actual processes.

These examples need clear records: the question, relevant account and product data, expected behaviour, observed result and reason for the verdict. Use test accounts and synthetic customer details wherever possible.

Judge readiness by the consequences of a mistake

A slightly awkward answer and a disclosure of private pricing should not carry the same weight in a launch decision.

Review less serious quality issues separately. Does the answer explain the relevant detail? Does it ask an unnecessary question? Does it send a straightforward product query to a person who should not be needed?

For objective checks, inspect the actual result: the enquiry record, account identifier, quantity or permission decision. For explanations, ask someone who understands the products and sales process to review whether the answer is useful and accurate.

Repeat important scenarios and try different wording. Keep some examples aside from day to day development so that the launch review includes questions the team has not repeatedly tuned against.

Passing these checks provides evidence for the scope you tested. It does not establish that every future conversation will succeed. Ordinary software testing, security review and operational monitoring still have their own jobs to do.

Launch one useful workflow and watch what happens

A practical starting point could be product questions and sales enquiries for one product category, with a clear route to a person when the assistant cannot help.

Decide who reviews failures, who corrects the underlying information and who can disable an action if something goes wrong. Keep enough diagnostic information to investigate problems while limiting unnecessary customer data in records.

Measure whether enquiries reach the right team with usable details. Review answer accuracy, appropriate handovers, response time and the cost of completing the task.

If you want to assess sales impact, compare outcomes carefully. A visitor choosing to use chat may already differ from one who does not, so a simple comparison between those groups is not proof of an uplift.

Revisit the tests when product information, policies, prompts, models or integrations change. Our guide to AI agents in production covers the broader operational work after a prototype.

Before building, choose one customer decision you want to support. Identify the information needed, the actions permitted and what evidence would convince your team that the workflow is ready.

That gives the project a concrete starting point and gives you something meaningful to test.