Reviews and ratings AI agents can read

"Which of these has the best reviews?" is one of the most natural questions to ask an AI shopping assistant. To answer it, the agent has to find your reviews, read them and trust them. Many stores load reviews in a way agents never see, or mark them up in ways that break the rules. Here's how to get it right.

Why reviews matter to AI agents

AI agents use reviews in two ways. The rating is a quick signal when they rank options: a 4.7 from several hundred reviews reads differently from a 4.7 from three. The review text answers specific questions the product description doesn't: "does it run small?", "is it loud?", "how long did the battery last after a year?".

If an agent can't see your reviews, it has nothing to weigh. It may fall back on reviews it finds elsewhere, or simply prefer a competitor whose ratings it can confirm.

Put reviews in the HTML

Most stores use a review app or service. Many of these load reviews with JavaScript after the page opens, from a separate server. A person sees them a moment later. An AI agent that reads the HTML without running JavaScript sees an empty space where the reviews should be, and many agents work that way.

• Check the source. Open a product page, view the page source (not the browser's inspector, which shows the page after JavaScript), and search for a phrase from one of your reviews. If it isn't there, agents that skip JavaScript don't see it.
• Ask your provider about server-side output. Many review platforms offer a way to include the rating and the most recent or most helpful reviews in the page HTML, through a platform integration or an API. Turn it on if yours does.
• At minimum, render the summary. The average rating and review count, in text near the product name, plus a handful of reviews. "Rated 4.6 out of 5 from 318 reviews" is easy for any agent to read.
• Link paginated reviews. If reviews run to several pages, use ordinary links between them so agents can follow them.

For the wider problem of content that only appears after JavaScript runs, see why AI agents can't see JavaScript-only content.

Mark up ratings and reviews

Schema.org gives agents the same facts in a standard form. On a product page, add aggregateRating for the summary and review for individual reviews, inside your Product JSON-LD:

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Ethiopia Yirgacheffe Whole Bean Coffee, 340 g",
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.6",
    "bestRating": "5",
    "reviewCount": "318"
  },
  "review": [{
    "@type": "Review",
    "author": { "@type": "Person", "name": "Dana R." },
    "datePublished": "2026-09-14",
    "reviewRating": { "@type": "Rating", "ratingValue": "5", "bestRating": "5" },
    "reviewBody": "Bright and floral as a pour-over. Arrived two days after roasting."
  }],
  "offers": {
    "@type": "Offer",
    "price": "18.00",
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock"
  }
}

Use reviewCount for reviews with text, or ratingCount if you count star-only ratings too. Say what scale you use with bestRating if it isn't 1 to 5.

The rules for review markup

Search engines publish rules for review markup, and following them is the safest guide for AI agents too. The key ones from Google's review snippet guidelines:

• Only mark up reviews people can see. Every review and rating in the markup should be readable on the same page. Markup for reviews hidden somewhere else, or that don't exist, breaks the rules and can get your structured data ignored.
• Mark up reviews of a specific thing. A product, a recipe, a course, a book. Not a category page summing up many products.
• Don't add review markup about your own business on your own site. Google calls reviews that a business hosts about itself, on its own site, "self-serving", and doesn't show review stars for them on LocalBusiness and Organization pages. The safe reading is: put ratings on your products, and leave your company-level reputation to independent review sites.
• Keep the numbers in step. The rating and count in the markup should match what the page shows, and update when new reviews come in.

Google's rules apply to its search results, not to every AI agent. But they describe what makes review data trustworthy, and agents that cross-check your markup against the page will notice the same problems.

Honesty is the strategy

AI agents read across many sources. If your site shows a perfect 5.0 and independent review sites say 3.2, the gap is itself a signal, and an agent may say so to the shopper. The only durable approach is reviews that reflect what customers actually think.

• Publish the negative reviews. Hiding everything below four stars makes the remaining rating less believable, to people and to agents. In the US, the FTC's rule on fake reviews and testimonials covers suppressing negative reviews as well as faking positive ones. Check the rules where you sell.
• Label incentives. If a reviewer got the product free or was rewarded for reviewing, say so next to the review.
• Mark verified buyers. A "verified purchase" label, applied honestly, helps agents weigh reviews.
• Reply in public. A clear reply to a complaint ("we've changed the packaging since March") is information an agent can pass on.
• Don't pool unrelated products. Showing reviews of an old model, or of the whole range, on a new product's page misleads. Pool reviews only across true variants, like sizes and colors of the same item.

What AgentScore checks

AgentScore doesn't have a dedicated reviews check. Its structured data checks look for schema.org JSON-LD on your home page, and for Product data with price, currency and availability on a product page, and flag markup that only appears after JavaScript runs. If your review app adds its markup with JavaScript, the same problem applies to your ratings. To see what an agent actually makes of your reviews, run a Ghost Agent journey such as "find the best-rated coffee under $20 and add it to the cart" and read the replay.

A short checklist

1. View the source of a product page. Are the rating, review count and some review text in the HTML?
2. If not, turn on your review provider's server-side or SEO output, or ask your developer to render a summary.
3. Check the Product JSON-LD includes aggregateRating that matches the page.
4. Remove any rating markup about your business as a whole from your own site.
5. Review your moderation policy: are you publishing negative reviews and labeling incentives?
6. Validate with Google's Rich Results Test.

Reviews work best alongside clear facts. See product descriptions AI agents can compare and product data that AI shopping agents can read.