Build an E-E-A-T Checker With AI: A Coding-Agent Approach

You can build an E-E-A-T checker with AI in an afternoon, and the crawler is the boring part. The hard, interesting part is what the agent does with a framework that has no score and no endpoint. E-E-A-T is not an API. It is a long reading exercise Google wrote for human raters, and that is exactly why a language model handles it better than a rules engine does.

Google added the extra E, for Experience, to its Search Quality Rater Guidelines in December 2022, and that revision pushed the document to 176 pages (Google Search Central, December 2022). Those guidelines do not directly influence ranking. Google uses them to evaluate its own ranking systems (Search Engine Land, December 2022). So there is nothing to query. There is only prose to reason over, and prose is the native input of a language model.

Why E-E-A-T has no API to call

Most SEO checks are lookups. Title length, canonical tags, structured-data validity, crawl depth. You write a rule, the page passes or fails, and a regex settles it. E-E-A-T is not built like that.

It is a judgment framework. Experience asks whether the author has first-hand exposure to the subject. Expertise asks about demonstrated skill. Authoritativeness asks whether the site is a known source. And Trust sits above the other three: Google calls Trust the most important member of the family, because a page that is not trustworthy scores low no matter how expert it looks (Search Quality Rater Guidelines, PDF).

None of those four reduce cleanly to a boolean. “Is this author experienced?” is a reading-comprehension question about a byline, a bio, an about page, and the tone of the writing itself. That is the kind of question an agent answers well. A validator cannot answer it at all.

How to build an E-E-A-T checker with AI

The pattern is now well documented. In August 2026, Search Engine Land published a walkthrough for building one with an AI coding assistant, packaged as a repository of instructions and skills you point at your own site (Search Engine Land, 2026). In their own test run, the audit flagged stale legal pages and an undisclosed lead-generation hub that a checklist would have walked straight past.

Here is the smallest version that earns its keep. Four files and a loop.

The four files

eeat-checker/
  crawl.mjs           # fetch one URL, strip it to readable text
  rubric/
    experience.md     # Google's guidance, verbatim, one file per dimension
    expertise.md
    authority.md
    trust.md
  audit.md            # agent instructions: grade the page, quote the evidence
  report.mjs          # collect verdicts, drop any that arrive without a quote

The rubric/ folder is the actual product. You copy Google’s own language into context, dimension by dimension, so the agent grades against the source instead of a summary of it. crawl.mjs is about thirty lines. audit.md is the prompt, and it stays short: read page.txt, work through each file in rubric/, and for every verdict quote the sentence on the page that wins or loses the point.

Only crawl.mjs is conventional engineering. The rest is why you reach for an agent and not a linter.

The grading loop

Run it one page at a time.

node crawl.mjs https://example.com/guide > page.txt
claude -p "Grade page.txt against rubric/experience.md. Quote one sentence per verdict."

Then report.mjs enforces the rule that keeps the whole thing honest. It walks the agent’s output and discards any judgment that shows up without a citation. No receipt, no point. That single constraint is the difference between an audit and a confident-sounding number.

Where an agent reads a framework better than a regex

Consider a single guideline: content should demonstrate that it was created by someone with real experience of the topic. A rules engine cannot test that. You would have to invent proxies. Word count, or the presence of an author schema. Every proxy is gameable and none of them measures the thing you actually care about.

An agent reads the page and asks the question directly. Does this review describe using the product, or does it paraphrase the spec sheet? Does the author page establish a reason to trust this person on this subject? The agent can quote the sentence that earns or loses the point.

This is the same lever we wrote about in how context shapes AI agent output: the framework you load as context, not the task prompt, decides the quality of the judgment. Load two paragraphs of paraphrased E-E-A-T and the agent grades against a vague memory. Load the real definitions and worked examples, and it grades against Google’s actual rubric.

There is a failure mode here worth naming. An agent asked to score without evidence will happily produce a confident number and no receipt. The fix is structural, not a matter of asking nicely. Require a quotation for every judgment. If the agent cannot cite the sentence on the page that supports a finding, the finding does not count, and the score drops accordingly.

In-platform vs standalone deploy, and where the split breaks down

A standalone checker points at any URL and reports what it finds. That is its strength. It has no privileged access, so it sees your site the way Google’s raters do, from the outside, working only from what the page actually renders.

An in-platform checker is different. When the E-E-A-T check runs inside the system that publishes the content, it stops being an audit and becomes a gate. This is how AstroAgent is built. The content pipeline scores a draft before it can publish, and a draft below the threshold is revised, not shipped. The check is not inspecting a finished site. It stands between the writer and the world.

The split breaks down in a specific place. A standalone tool can tell you a page lacks demonstrated experience, but it cannot fix the cause, because the cause is upstream in how the content was commissioned. An in-platform check can. It knows the author identity from configuration, it knows the audience, and it can refuse the draft and send it back naming the exact dimension that failed. We covered why that upstream context is decisive in the data foundation most teams skip: an agent inherits the quality of what it is given before it writes a word.

So the split is really diagnosis versus prevention. A standalone checker diagnoses a site you already shipped. An in-platform gate stops the low-E-E-A-T draft before it ships.

What the checker cannot tell you

One caution. An E-E-A-T checker measures whether a page signals experience, expertise, authority, and trust. It does not measure whether those signals are true. An agent can confirm that a bio claims fifteen years of practice. It cannot confirm the fifteen years. Trust, in the end, is earned off the page, in citations and a track record the crawler never sees.

Start with the framework, not the crawler

If you build one this month, spend your time on the rubric, not the plumbing. The crawler is a solved problem. The quality of the audit is set almost entirely by two things: how faithfully you load Google’s actual guidance as the agent’s context, and how strictly you force it to cite evidence for every call it makes.

That is also the clearest way to evaluate a content platform. Ask how it reasons over an unstructured framework, not whether it exposes a tidy SEO score. If you want to see the pattern running inside a full pipeline, the rest of our engineering write-ups trace how the same reasoning gates every article this site publishes.