9 Steps to Optimize for AI Agents in Ecommerce & Protect Revenue

What is an AI agent in ecommerce and what do they do?

AI agents in ecommerce are digital assistants to human shoppers that can complete shopping tasks on their behalf. They can help research brands and products, filter and compare options, answer questions, check availability, build carts and help move purchases toward checkout, and in some cases, complete the transaction if delegated to do so.

A shopper might ask an AI agent: “What are the best gifts for my 7-year-old nephew who likes making art but already has a lot of crafting supplies?” Or they (okay, me in this scenario) may upload a selfie and ask: “Help me find a dupe of these sunglasses that are the same shape, black, under $150 and fit my face better.”

Rather than typing a few keywords into a search bar, shoppers give AI agents a shopping brief with context, constraints and intent. The agent’s job is to interpret that brief, compare options and decide which products or brands are the best fit for the human buyer’s request.

As AI agents take on more of that work, from product research to cart-building and transaction support, they become part of the broader shift toward agentic commerce.

How are AI agents different from traditional automation?

Traditional automation responds to what a shopper does and supports steps in the journey. AI agents can interpret what the shopper wants and influence what they see, compare and consider next.

Traditional automation follows strict “If X, then Y” logic. If a shopper abandons a cart, then send an email. If an item is back in stock, trigger a notification. If a customer views one product, recommend a related or “People also viewed” one. In short, the system responds to a specific action with a predefined next step.

AI agents don’t work that way in ecommerce. They don’t react to a click or apply a rule. Instead, they interpret a shopper’s goal, evaluate available information across thousands of pages on the web and decide what steps to take next.

Take the sunglasses example from earlier. A recommendation engine might show similar sunglasses based on the product viewed. An AI agent can interpret the shopper’s photo, understand that they want the same shape in black, stay under $150, factor in the face fit and return a small selection of products that match the entire request.

For merchants, that changes the role your site plays in the shopping journey. Your category pages, product filters and comparison tools may still matter, but they may no longer be where the shopper does most of the narrowing down.

SEO vs. AEO in ecommerce: Why you need to “codify your vibe” for agents

Ecommerce SEO has traditionally been about helping shoppers find your products through search engines. Merchants optimized product pages, category pages and educational content around the keywords people typed into a search engine like Google or Bing.

With ecommerce AEO, you can’t take that same approach. Instead of asking, “How do we rank on the first page of search results?” you have to ask, “How do we help AI systems understand the shopper’s goal, compare the options accurately and recognize when our brand or product belongs in the answer?”

Alicia Chaffee, head of strategy at Dôen, describes this as needing to “codify your vibe.”

Everything a brand wants a human to feel on its website now needs some kind of tag, language or code so an AI assistant can pick it up. That may feel clinical, especially for brands built around emotion, taste and visual storytelling. But it doesn’t mean replacing the human experience — it means creating a machine-readable layer beneath it.

For example, if your brand is known for romantic event dresses, that concept cannot live only in photography, campaign copy and styling. It needs to show up in product attributes, fit notes, occasion-based copy, structured data, reviews, FAQs and category language. An AI agent needs to understand that a certain SKU you sell isn’t just a “strapless yellow floral midi dress.” It’s also a summer wedding guest dress under $120 and a lightweight, chiffon vacation dinner dress.

The same is true across categories. If your product is durable, explain what makes it durable. If your return policy is easy, make that clear. If your product is better for gifting, sensitive skin, petite sizing, travel, installation or repeat use, say so in language AI systems can parse.

How to audit and optimize your ecommerce store for AI agents

Optimizing for AI agents starts with a simple question: When AI systems describe your brand, products and policies, do they get the important parts right?

Step 1. Identify what your brand needs to be known for

Before you test how AI agents understand your store, get clear on what they should understand.

Pick two or three things your brand should be known for in a customer’s shopping journey. These should be specific enough for an AI system to match to a shopper’s request. For example, a fashion brand might want to stand out for petite sizing, formal dresses under $300 and fast two-day shipping. A skincare brand might be known for sensitive skin and fragrance-free formulas.

Avoid broad claims like “high quality,” “customer-first” or “premium.” Those may be true, but they are too vague to help an AI agent understand when to recommend you.

Step 2. Ask AI assistants how they describe your brand

Next, do an AI brand visibility audit to test how AI systems already understand your brand. Use the same kinds of questions a real shopper might ask, like:

• “What is [YOUR BRAND NAME] best known for?”
• “Who is [YOUR BRAND NAME] best for?”
• “What are the pros and cons of buying from [YOUR BRAND NAME]?”
• “Compare [YOUR BRAND NAME] to [COMPETITOR] for [CATEGORY].”
• “What are the best [CATEGORY] brands for [SPECIFIC USE CASE]?”
• “Which brand should I choose if I care about [PRIORITY]?”
• “Why might someone choose [COMPETITOR BRAND NAME] over [YOUR BRAND NAME]?”
• “What would knock [YOUR BRAND NAME] out of consideration?”
• “Does [YOUR BRAND NAME] have easy returns?”
• “Does [YOUR BRAND NAME] ship quickly?”
• “What do customers complain about with [YOUR BRAND NAME]?”

Run these prompts across multiple AI assistants — like ChatGPT, Claude, Gemini, Perplexity, etc. — to see how different systems interpret your brand. And when possible, run these tests from clean or neutral accounts that aren’t tied to your company, work history or previous brand research. Agents tend to have “personalization bias,” meaning logged-in accounts are likely to produce answers shaped by prior chats or saved memory, which can skew the types of answers you get back.

For example, if you have spent weeks asking an AI assistant about your brand, your competitors or your product category, that account may already have more context than a typical shopper would. Because of that, your brand may be more likely to appear or be described favorably in that account than it would be for a shopper with no prior history.

Step 3. Look for what AI agents get wrong, miss or overstate

Once you have the responses, audit them closely. Analyze whether the agent:

• Understands what you sell
• Describes your brand or product accurately
• Identifies the right customer, use case or buying occasion
• Confuses you with a competitor
• Cites outdated pricing, policies, brand or product information
• Misses your strongest differentiators
• Recommends your competitors over you
• Makes claims your brand would not make

Step 4. Strengthen the pages AI systems are likely to use and connect product features to shopper intent

Once you know what AI assistants are missing, improve the source material. Start with the pages most likely to shape how AI systems understand your store. These can include product detail pages (PDPs), category pages, FAQs, marketplace listings, policy and warranty pages, reviews, about pages, schema markup, etc.

Once you’ve identified the pages that need work, update them with clearer, more specific information. For PDPs, for instance, make sure shoppers and AI agents can quickly understand the most important product details, in the product title, short description, feature bullets, specs, size guide, FAQ and/or structured data.

A backpack PDP, for example, should quickly get across:

• What the product is: a 30L backpack that fits most carry-on dimension requirements for airlines
• Who it’s for: first-time hikers and outdoorsy weekend travelers who want one bag for short trips
• What use case it solves: packing for a weekend trip, carrying gear comfortably or avoiding checked luggage
• What colors it comes in: black, navy blue, gray
• What sizes it comes in: S/M, L/XL
• What it’s made from: water-resistant recycled nylon

Once those core details are clear, connect the features to the reasons shoppers buy. A specs table can say the backpack is made from water-resistant recycled nylon, comes in S/M and L/XL sizes and has a 30L capacity. The product description or feature bullets can explain why those details matter: water-resistant materials help protect clothes, electronics and trail essentials during light rain or unpredictable travel days; multiple sizes help shoppers find a better torso fit for longer walks, hikes or travel; and 30L capacity gives day hikers and weekend travelers enough room to pack for short trips.

Step 5. Make your policies clear enough to be summarized accurately

Agents also compare the shopping experience around products they’re recommending. That includes shipping, returns, refunds, warranties, customer support, delivery timelines and store availability. If those details are vague or inconsistent, AI systems may summarize them incorrectly or just leave them out.

Audit your policy pages for questions like:

• How long does shipping usually take?
• Is expedited shipping available?
• Are returns free?
• What is the return window?
• Are final sale items clearly labeled?
• How long do refunds take?
• Are exchanges allowed?
• What happens if an item arrives damaged?
• Is there a warranty?
• How can a customer contact support?

Step 6. Clean up outdated or inconsistent product information

Sometimes AI assistants surface information from old or incomplete sources. Other times, they misread a source, overstate a claim or hallucinate details that aren’t supported at all. This is especially common with product specs, pricing, shipping and product page URLs.

If your audit found that agents were surfacing outdated or incorrect information, treat it as a source-material problem to investigate. Ask the AI assistant where it found the information and request a link to the source. Then click the link to confirm whether or not the source actually says what the assistant claimed.

If the source supports the false or outdated claim, fix the source. If you control it — it’s a brand-owned landing page, PDP, FAQ or old blog post — revise it or redirect it to a relevant page. If it’s third-party content — like a product or brand mention in another company’s blog post, gift guide or marketplace listing — contact the author, publisher, partner or marketplace owner and ask for a correction and provide the updated information.

On the other hand, if the linked source doesn’t support the claim, document the hallucination and look for other pages that may be causing confusion. The issue may not be one bad page. It may be inconsistent language across several pages, an old product feed, outdated schema, stale marketplace copy or third-party content that no longer reflects what you sell.

Step 7. Add or update existing structured data

Structured data helps search engines and AI systems understand what’s on a page. Use markup and product feed data to reinforce the details that matter most, like product name, brand, category, price, availability, color, size, material, reviews, ratings, shipping details, return details and FAQs. Make sure those details match what shoppers read as they’re browsing your site.

If you already have markup on your website, and most ecommerce sites do, audit it before adding anything new. If your structured data lists the wrong color, misspells a product name or shows a size that is no longer available, agents have more room to misread or misrepresent the page.

Use Google’s Rich Results Test to check your schema markup across your public pages. For help writing schema, leverage Google’s Product structured data documentation and Schema.org’s documentation.

Step 8. Review your checkout and fraud rules

AI agents change what a legitimate buying session looks like. A shopper may spend 20 minutes comparing products through an AI assistant, then land directly on a PDP and check out quickly. To a rules-based fraud system, that session might look thin: low page depth, short time on site, little browsing behavior and limited comparison activity. Those signals still matter, but they need more context.

Review any fraud rules or manual review triggers that heavily penalize fast checkout, direct-to-product visits, low click depth, unfamiliar referral paths or other behavior that could become more common when agents help shoppers narrow their choices before they reach your site.

At the same time, don’t assume agent-like behavior is safe by default. Fraudsters use automation too, and many of the surface-level signals can overlap: fast sessions, limited browsing, unusual device patterns, new accounts or mismatched customer details. A legitimate shopper using an AI assistant and a bad actor using automation may both look “nontraditional” at first glance.

Because of that, session behavior alone won’t tell you the full identity story. You need to evaluate the shopper, account, device, payment method, delivery address, order history and post-purchase behavior together. Consider using Signifyd’s Commerce Protection Platform to connect those signals, and give your risk team more context for both human- and agent-led sessions.

Step 9. Build AI visibility testing into your regular workflow

You shouldn’t treat AI visibility as a one-time brand audit. Instead, build it into the workflows you already have.

Rerun your priority prompts quarterly across the AI assistants your customers use. Compare the results against your previous audit to see what changed, what improved and what still needs work. Also remember to add and track new prompts as your assortment, policies, competitors and customer questions change.

Clearer signals lead to better AI recommendations

Ecommerce AI agents can only work with what they can understand. If your product details are thin, your policies are inconsistent or your differentiators are buried in visuals alone, your brand may be harder to recommend, even when your product is the right fit.

The same applies at checkout. As AI-assisted journeys become more common, you’ll need enough context to know whether a fast, direct session reflects legitimate buying intent or risky automation. Signifyd’s Commerce Protection Platform helps merchants make those decisions and protect accounts with a fuller view of identity, payment, device, order and behavioral intelligence across the customer journey.

Photo by Getty Images

For more on how retailers are thinking about AI agents, brand experience and the need to “codify your vibe,” watch the full Digital Edge episode with Signifyd’s Ellie Ronning and special guest Alicia Chaffee.