The AX Audit inspects supported public websites for technical agent readiness. Its deterministic checks produce an AX Score, Readiness Layers, and Findings. The scan does not change the site or prove that a particular assistant will recommend it.
Different clients read and interact with websites differently. The audit checks exposed technical signals; it does not reproduce every model, browser, search engine, or authenticated customer journey.
Current AX Audit dimensions and weights
| Dimension | Weight | Readiness Layer |
|---|---|---|
| Crawlability | 15 | Access |
| Structured data | 13 | Identity |
| Actionability | 12 | Agent integration |
| Agent interaction | 10 | Agent integration |
| Discoverability | 8 | Discovery |
| Trust signals | 7 | Identity |
| Content quality | 6 | User experience |
| Performance | 6 | Access |
| Reachability | 6 | Access |
| Authoritativeness | 5 | Identity |
| Freshness | 4 | Discovery |
| Security & trust | 4 | Access |
| Usability | 4 | User experience |
These product-defined weights come from the current audit implementation. Each scored dimension contributes its earned fraction multiplied by its weight. A dimension with no applicable score is left out of its Readiness Layer's available weight.
Why a separate score from classical SEO
Classical SEO tools (Lighthouse, Ahrefs site audit, Screaming Frog) optimise for one consumer: a search-engine crawler that renders pages, builds an index, and ranks results for a query. They were built for the SERP era. They're still useful, but they don't tell you what an AI agent — sent by a user to actually accomplish something on your site — sees and can do.
Inspect crawler rules, readable content, and supported task interfaces as distinct concerns. Training crawlers, search crawlers, and user-initiated browsing can have different user agents and policies; blocking one does not establish universal invisibility.
Crawlability: Inspect Access and Rendering
Review robots.txt together with hosting and bot-protection rules. A crawl restriction may be intentional; decide which clients should have access before changing it.
Crawlability combines robots rules, page access, JavaScript-rendering dependency, cookie walls, and bot protection. The total effect of a change depends on the applicable checks; this article does not promise a fixed point increase for allowing one crawler.
The other half of crawlability is render-blocking: pages that require JavaScript execution to surface their main content, or that gate content behind interaction. Clients vary in rendering support. Inspect what the initial HTML exposes, and test the intended client when an important task depends on JavaScript.
Dimensions 2 + 3: Discoverability and structured data
Discoverability is sitemap and llms.txt territory. Sitemaps you already know — keep each file within the sitemap protocol's limit of 50,000 URLs, segment by content type, refresh on publish. The newer signal is llms.txt, a flat-file convention that lets you tell AI agents which URLs to prioritise reading. It is an optional documentation proposal, not a universal discovery standard or a crawler-permission file.
Structured data is JSON-LD coverage — the schema.org types that turn a paragraph of prose into a machine-readable claim. We check for Article, FAQPage, HowTo, SoftwareApplication, and Product where each is appropriate, and we check that the JSON-LD agrees with the visible page (a common bug: the article body says "updated 2026-04-15" and the JSON-LD says "2024-09-01" because the schema is templated and the date isn't injected). We also flag missing sameAs entity links — they're cheap to add and they let agents disambiguate which "Dashform" or "Acme Corp" you mean. For an example of how a methodology document combines structured tables with original prose, see our form-builder evaluation rubric.
Content Quality: Inspect Readable Structure
Content Quality uses structural checks rather than a claimed Google ranking penalty. It looks at semantic HTML, image alt text, extractable text, FAQ signals, heading hierarchy, and language declarations.
Accurate bylines, dates, and primary sources are useful editorial practices, but do not confuse those practices with the Content Quality dimension's implemented structural checks.
Read the specific Finding before choosing a fix. Better prose and better markup can address different problems and need separate review.
Dimension 5: Agent interaction — the new frontier
Agent interaction is the dimension most sites haven't thought about. Classical SEO ends at "can the page be read?" Agent interaction asks the harder question: "can a third-party agent, sent by a user, complete a task on this site?" That task might be filling a form, booking an appointment, comparing a product, or pulling a structured payload.
We score three sub-signals. MCP server presence — does the site expose a Model Context Protocol endpoint that an agent can speak? Public API discoverability — is there a documented API at a stable URL, with auth that an agent can negotiate? Structured form schemas — for sites with primary forms (signup, contact, product configuration), are the forms machine-readable so an agent can submit them on a respondent's behalf? The third sub-signal is where Dashform's own bias shows: we built the company around the idea that forms should be agent-fillable, and most of the form-builder category is not.
A detected endpoint is evidence of an exposed interface, not a successful transaction. Test supported actions through a compatible client and inspect the resulting reply or external record.
Dimension 6: Security and trust
The Security & Trust dimension checks HTTPS and the presence of privacy-policy and terms links. These signals do not establish that a site is secure or that its legal terms are appropriate.
The link-presence check does not interpret the legal meaning of a privacy policy or terms of service. Have the appropriate reviewer assess those documents independently.
What we deliberately don't score
Keep these limits in mind when interpreting the report:
Performance. The current AX Score includes a Performance dimension. It does not replace a dedicated performance or Core Web Vitals assessment.
Search rankings. The audit does not reproduce search-engine or AI-answer ranking systems. Its own weights are not claims about how those systems rank content.
Usability. The audit includes a Usability dimension, but browser agents and human visitors still need task-specific testing on the actual page.
Cadence and updates
Use the scan date and current report when comparing results. Changes to a website or the audit implementation can change its score; this article does not promise a future release schedule or weighting change.
Frequently Asked Questions
Why do the AX Audit's weights add up to a round total when the form-builder rubric's don't?
The AX Score summarizes weighted technical checks on an absolute scale. It is not a ranking percentile or a prediction of commercial outcomes; the report presents grade bands separately from its Benchmark Rank.
Does AX Audit work for sites in any language?
Many checks inspect technical signals such as robots.txt, JSON-LD, and links rather than language. Heuristics can miss valid content, so inspect the Findings and evidence for your site rather than assuming equal accuracy across languages.
How is this different from Lighthouse or Ahrefs?
Lighthouse and Ahrefs are excellent for classical SEO and human-user experience. AX Audit measures something they don't: how prepared the site is for AI agents and AI-grounded search. We recommend running both — they answer different questions, and a site can score 95 on Lighthouse and 50 on AX (or vice versa).
My site uses Cloudflare's bot management — does that affect the audit?
Maybe. Cloudflare's AI bot blocking is enabled by default on many plans and silently drops requests from GPTBot, PerplexityBot, ClaudeBot, etc. before they reach your site. We detect this when our crawler is challenged or rate-limited; the audit report will flag it as a crawlability issue with a recommendation to allow-list the AI user-agents you want to be readable by.
Do you store the URLs people audit?
Yes — we store the URL and the audit result so users can return to a previous report and compare against later runs. We don't store credentials, request headers, or any private content. The audit itself only fetches public pages with a clearly identified user-agent.
How often should I re-run the audit?
Re-run after changes to access rules, rendering, structured data, discovery files, or agent tools. Choose a review cadence based on how often your site and its dependencies change.
Can I get the report as a PDF or share it with my team?
Each AX Audit Report has a shareable public URL. Use that report and its scan date when discussing Findings with your team; no PDF release date is promised here.
What's a "good" AX score?
Use the report's grade and individual Findings to prioritize relevant fixes. A grade does not establish a percentile, a particular blocker, or guaranteed agent access. Use the stated Benchmark Rank population for relative comparisons.
Conclusion
If a site's AX Audit produces a number, this rubric is the receipt. Use its weighted dimensions and individual Findings to decide what to inspect next. We'd rather you read this and then run the audit than the other way around. The audit is free, no account required, and runs against any public URL — your own site, a competitor's, a candidate vendor's during procurement.
