How ready is your site for AI agents?

Free, no account, no tracking. We run the same Lighthouse audits the census runs on 15.5 million sites, compare your figure against them, and tell you which dimension gives back the most if you fix it first.

Site URL

What gets measured, and what each one weighs

Why these four, and why one of them multiplies instead of adding

Read carries more than half of the score because it is the precondition: an agent that cannot parse the page can do nothing with it.

Three of the four dimensions add up. The fourth multiplies: how much of the site is reachable by the AI agents we track. It works that way because it is a precondition, not an improvement — if an agent cannot fetch the page, its accessibility tree does not matter — so a site that turns away every agent scores zero however good the rest of it is. Blocking used to EARN points here, under the reasoning that having decided was a sign of maturity. In an index that measures how ready a site is for AI agents, that was backwards. And it is not a value judgement: readiness is a matter of fact, and whether you want to be ready is your call. A low score here means you chose to be closed, not that you did something wrong.

Poor19.9%

AWCS

What the AWCS is, and how it is measured

Measured on 14,373 pages, 95.8% of which carried the agentic category, with a confidence margin of ±0.44.

14.3%19.9%

Read

What Read measures

Lighthouse builds the accessibility tree an agent would traverse and scores the page pass or fail. The denominator is only the pages the audit could evaluate: a notApplicable or an error is excluded rather than counted as a failure, and the fraction that could be evaluated is published beside it as the coverage.

n=14,374 · coverage 95.8% · margin ±0.44 pp.

53% of the score

Weak30.4%

▲ +1.3 pp

Guide

What Guide measures

The llms-txt audit passing: a file is served AND Lighthouse considers it valid. The denominator is every sampled page, so this is a fraction of the whole web and not of the sites that have a file. This is the figure the index uses, because a broken file does not guide an agent.

n=14,373 · coverage 95.8% · margin ±0.44 pp.

27% of the score

Poor13.1%

= +0.1 pp

Act

What Act measures

A count, not a percentage: how many sampled sites register WebMCP tools that an agent can call. It is the audit that defines the Act dimension — the other two WebMCP audits measure how good those tools are, not whether they exist.

n=14,374 · coverage 95.8% · margin ±0.44 pp.

20% of the score

Very poor5.1%

▲ +0.6 pp

Reachable

What Reachable measures

Read from the robots.txt of the sampled sites: of the AI agents we track, how many are NOT disallowed. Gradual and not a yes or no — turning away one agent out of seventeen is not the same as turning away all of them. A site with no robots.txt blocks nobody, so it counts as fully reachable: that is a measurement, not a gap. This is the only figure that MULTIPLIES the index instead of adding to it, because it is a precondition: if an agent cannot fetch the page, nothing else about the page matters. One caveat we would rather state than hide: a blanket Disallow under User-agent: * is not counted, because the corpus-wide measurement cannot see it either, and we would rather be consistent than clever.

n=15,009 · coverage 100.0% · margin ±0.43 pp.

multiplies the rest

Excellent96.0%

= −0.2 pp

What we do to your site, exactly

Two requests. Google PageSpeed Insights loads your home page and runs Lighthouse on it — the same tool, the same version as the census. And we read your robots.txt, identifying ourselves, because that is where the Allow dimension lives and Lighthouse does not audit it. Nothing else is touched, nothing is stored about you, and the report is not indexed: it is about someone else’s site and we do not put those in Google.

From Omitsis, who publish this census

Or skip the reading

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