From a score of 36 to 85+: what actually raises an agent-readability score

The short answer: a low agent-readability score almost never comes from the content itself — it comes from the layer around it, the files and rules an AI agent consults before it even reads your pages. In one of our own runs with the Agentic Search Optimizer, an online store scored a 36. After the recommended fixes — all shipped through an edge worker, without touching a single line in the CMS — the score climbed to 85+.

That gap wasn't rewritten copy. It was three technical layers invisible to a human visitor: access for AI crawlers, machine-readable structure, and a navigation file that tells a model where to look. Below is what a score of 36 actually reveals, and which fixes made the difference.

What a score of 36 actually means

The Agentic Search Optimizer fetches a site the way an AI agent does — not the way a human sees it in a browser — and runs it through 15+ parallel checks: crawler access, schema.org data, agent discovery, robots rules for 19 known AI crawlers, and how machine-readable the content itself is. A score in the thirties almost always points to the same pattern: the site reads fine for humans and for Google, but is invisible or ambiguous to the crawlers and agents searching on behalf of an AI assistant. No llms.txt, missing or incomplete schema.org markup, robots rules that say nothing specific about AI crawlers, and content that technically exists but isn't structured predictably.

That's a different problem than a classic SEO audit surfaces. A site can score well on speed and indexability — the classic foundation — and still be unreadable to a language model that needs structure and explicit markup to understand what a page is, not just that it exists.

The fixes that made the difference

1. Robots rules for AI crawlers. A robots.txt written only with Googlebot in mind often locks AI crawlers out by accident — or leaves them unaddressed, which some agents read as caution. Explicit rules for the known AI crawlers were the first and cheapest fix.

2. Schema.org markup. Structured data tells a machine explicitly what a page is — product, article, organization — instead of making it guess from running text. For an online store, that's the difference between an agent recognizing a product page and citing it correctly, versus an agent that only sees a block of text.

3. An llms.txt. This navigation file points a model straight at the pages that matter, instead of forcing it to crawl the entire site itself. How that file works, and whether your site needs one, is covered in our piece on llms.txt.

4. Agent discovery. Like our own reference implementation — which uses DNS-based agent discovery and a machine-readable service description — this case also got discovery records that let an agent determine what the site offers immediately, without having to search first.

Why an edge worker, not the CMS

The most notable choice in this case was practical, not technical: every fix ran through an edge worker sitting between the visitor (or agent) and the existing CMS, instead of going through CMS changes directly. That turned a weeks-long implementation into days — no migration, no developer having to learn an unfamiliar CMS, no risk of a content change breaking a template. The edge worker adds the missing layers — headers, robots rules, schema.org, llms.txt — on top of what's already running. For a site that can't simply be rebuilt, that's often the only realistic route to a fast score jump.

What this score does and doesn't guarantee

A high agent-readability score closes the easiest leak: a model that simply can't read a site can't cite it either. It doesn't guarantee a mention — a technically flawless site can still be skipped if a competitor gives a more convincing answer. Technical readability and actually being cited are two separate measurements; how to check the latter yourself, periodically, is covered in our piece on measuring AI visibility.

Conclusion

The gap between a score of 36 and 85+ wasn't new content — it was four invisible technical layers that most sites simply never set up: crawler access, schema.org, an llms.txt and agent discovery, all addable without rebuilding the existing CMS. How that technical foundation combines with the editorial side into a complete GEO picture is covered in our pillar article on GEO.