The agentic readiness checklist
AI agents read your site through the accessibility tree, structured data and discovery files - not a rendered screenshot. This checklist covers the 30 concrete signals we measure across thousands of audits, grouped the way agents actually consume a site. Average score across our audited directory: under 50% - most of the web is not ready.
1. Agent interaction - can a machine parse your pages?
- Semantic HTML landmarks -
<main>,<nav>,<article>, headings in order,langon<html>, alt text on images. The accessibility tree IS the agent's data model. - Clean, crawlable markup - real title and meta description; content present without JavaScript execution.
- Low extraction friction - answers near the top; short first paragraphs; no jargon walls.
- Indexability - no accidental
noindex/robots blocks; being indexable is the precondition for appearing in AI answers. - One canonical URL per page - agents must not see duplicates.
- Layout stability & weight - explicit image dimensions (no layout shift), lean DOM, minimal render-blocking resources.
2. Machine extraction - can an agent quote you?
- Real HTML tables and lists for comparisons and specs - agents extract structure, not screenshots of it.
- Atomic sentences - self-contained statements an agent can lift without losing meaning (target: 40%+ of sentences).
- Entity density - name the products, places, people and numbers; vague copy is unquotable.
- Definition patterns - clear "X is ..." sentences let agents answer directly.
- Answer capsules - open each section with a 15-30 word self-contained answer.
3. Structured data - facts machines can consume
- Schema.org JSON-LD - at minimum Organization/WebSite; product, article and FAQ types where relevant.
- Breadth - rich properties (address, logo, sameAs, dates), not empty stubs.
- Speakable markup for voice surfaces where it fits.
4. AI discovery - can agents find and use you?
- llms.txt - the curated machine summary (full guide). Only 6.4% of 48,005 sites we probed have one.
- robots.txt rules for AI crawlers - explicit decisions for GPTBot, ClaudeBot, PerplexityBot (guide).
- sitemap.xml with lastmod - let machines enumerate and re-crawl you efficiently.
- A content feed (RSS/Atom) - agents track changes.
- Language/locale signals -
lang, hreflang. - Stated AI-use permissions - an ai.txt or licensing note removes ambiguity.
5. Trust - would an agent transact with you?
- Consistent identity - name, address, phone consistent everywhere; Organization schema anchoring it.
- Visible authorship and dates - humans and machines both read them as accountability.
- Methodology transparency - show how content/products are produced, tested or reviewed.
- AI-assistance disclosure where a reasonable reader expects it.
- Strong titles/meta - unique, descriptive, well-sized.
- Freshness - re-date and update key pages; stale sites read as abandoned.
Longevity, HTTPS and current activity also feed trust: an agent (or its user) weighs "how long has this site existed and is it maintained" before transacting - we surface these on every profile.
Score yourself
Every item above is measured automatically in our free audit - pass/fail with the specific finding and the highest-impact fixes ranked.
Run the 30-check audit on your domain
Method details: methodology. See who leads: most AI-ready websites.
Frequently asked
- What is agentic readiness?
- How well a website can be read, trusted and transacted with by AI agents - measured through concrete signals like structured data, semantic HTML, discovery files and trust markers, not opinions.
- Is agentic readiness the same as SEO?
- They overlap (clean HTML, structured data help both), but agentic readiness centers on machine consumption: the accessibility tree, machine-readable summaries, explicit crawler permissions and quotable content.
- How is an agentic readiness score calculated?
- On MarkosWeb, as a fraction of passed deterministic checks - like Google Lighthouse’s agentic-browsing scoring - never an invented 0-100. Scores can be improved by fixing the failed checks, never bought.