How AgentReady scores your site
7 dimensions. 100 points. Each one reflects a specific step in the AI agent buying journey — from initial discovery through to autonomous transaction.
Version 1.0 · Updated May 2026 · Scores recalculated monthly
The agent buying journey
When a buyer delegates a purchase to an AI agent, it executes this sequence. Your score reflects how far it gets before it fails — or succeeds.
Discovery
User asks the agent: "Find me the best waterproof dog bed under $100 with free returns."
Dimensions tested
Agent identifies candidate brands from training data and live search.
Evaluation
Agent crawls shortlisted sites, extracts product specs, pricing, and trust signals.
Dimensions tested
Agent scores each brand on completeness of extractable evidence.
Recommendation
Agent builds a shortlist ranked by confidence. It needs proof to justify its top pick.
Dimensions tested
Agent presents a ranked recommendation with supporting evidence to the buyer.
Transaction
Buyer approves. Agent executes the purchase — or stalls because it can't find a clear path.
Dimensions tested
Purchase completes — or agent returns to the buyer with a friction point.
🔒 Human Approval Gate
Before any agent completes a purchase, the buyer confirms. Your score predicts how likely they are to approve: high-scoring sites give agents complete, trustworthy evidence — buyers approve quickly. Low-scoring sites produce vague recommendations — buyers hesitate or reject.
Score bands and approval rates
Each tier represents a distinct agent capability level, mapped to a measurable human approval rate.
You win in autonomous commerce.
approval rate
Agents prefer you â buyers approve.
approval rate
You're in the recommendation â not always the winner.
approval rate
You're understood, but not recommended.
approval rate
You're found but not understood.
approval rate
You don't exist in AI commerce.
approval rate
The 7 dimensions in detail
Each dimension measures a distinct capability in the agent buying journey, with weighted sub-signals.
Machine Readable
/18 ptsCan an AI agent parse your products without guessing?
Machine readability is the foundation. Without structured data, agents treat your site as an unstructured blob of text — they might guess at prices and product names, but they won't commit that guess to a recommendation. JSON-LD Product schema gives agents exact pricing, availability, condition, and brand in a format they can parse in milliseconds.
Sub-signals tested
JSON-LD Product schema present
Does every product page emit valid schema.org/Product markup?
Price & availability accuracy
Are price, priceCurrency, and availability fields populated and current?
Breadcrumb + site navigation schema
Can agents map your catalogue hierarchy from BreadcrumbList markup?
Organization / LocalBusiness schema
Is your business identity declared with schema at the site root?
FAQ and HowTo schema (where applicable)
Do support pages and product guides use structured markup?
Schema validation (no errors)
Does schema.org validation return clean output with no critical errors?
Quick fix: Install a schema plugin or add JSON-LD Product blocks to your product template. Validate at schema.org/validator.
Actionability
/18 ptsCan an agent actually buy — not just browse?
Reading your site is step one. Acting on it is step two. Actionability measures whether an agent can find and execute a purchase path: add to cart, proceed to checkout, complete a transaction. Without explicit action pathways, agents stall at the research phase and never convert. /llms.txt is the new robots.txt — it tells agents what they're allowed to do on your site.
Sub-signals tested
/llms.txt published
Have you declared agent permissions, allowed actions, and contact info at /llms.txt?
PotentialAction schema on product pages
Do product pages declare a BuyAction or OrderAction with a target URL?
Cart/checkout API or Headless endpoint
Is there a programmatic path to add items and initiate checkout?
Contact and support action paths
Are ContactAction or CommunicateAction schemas declared for agent queries?
Search action schema
Does your site declare a SearchAction so agents can query your catalogue?
Agent-accessible order status
Can agents check order status on behalf of customers?
Quick fix: Publish /llms.txt (template at llmstxt.org). Add PotentialAction schema to product pages with a target checkout URL.
Entity Consistency
/18 ptsDoes every source agree on who you are?
AI agents are trained on the web. If your business name appears differently across sources, agents see multiple possible entities and lower confidence scores across all of them. Entity consistency is the trust layer — it tells agents that your site, your Google Business profile, your LinkedIn, and your schema declarations all refer to the same verified business.
Sub-signals tested
Business name consistency (site vs Google vs LinkedIn)
Does your exact brand name match across all major indexed sources?
Phone number NAP consistency
Is the same phone number on your site, Google Business, and directories?
Address consistency
For businesses with a physical location: does your address match exactly?
Logo URL stability
Does your schema Organization entity reference a stable, crawlable logo URL?
Social profile cross-links
Are your sameAs links in Organization schema pointing to active, verified profiles?
Review profile consistency
Does your brand name on Trustpilot, Google Reviews, and G2 match exactly?
Quick fix: Audit your name, phone, and address across Google Business, LinkedIn, Yelp, and Trustpilot. Update your Organization schema sameAs array to link them all.
Content Extractability
/14 ptsCan agents read what you've written?
JavaScript-rendered content is invisible to most AI crawlers. If your product descriptions, specs, and pricing are injected by React or Vue after page load, agents see only an empty shell. Similarly, an overrestrictive robots.txt blocks agents from the pages that matter most. Content extractability measures whether your actual words are available to agents at crawl time.
Sub-signals tested
robots.txt allows AI crawlers
Are GPTBot, ClaudeBot, anthropic-ai, and PerplexityBot allowed on product pages?
Server-side or static rendered content
Are product names, prices, and specs in the raw HTML — not injected by JS?
Content density (text/HTML ratio)
Is your page content substantive, or mostly navigation and boilerplate?
Image alt text completeness
Do product images have descriptive alt text agents can use as product context?
No aggressive bot blocking (Cloudflare/CAPTCHA on crawl)
Are AI crawlers reaching your pages, or being blocked by WAF rules?
Sitemap coverage
Does your sitemap.xml include all product pages agents need to discover?
Quick fix: Allow GPTBot and ClaudeBot in robots.txt. Ensure product data is server-rendered. Check your CDN/WAF isn't blocking AI user agents.
AI Visibility
/14 ptsDo the AIs already know you exist?
AI training data is not just the web — it's the web as it was indexed, cited, and discussed in high-quality sources. If ChatGPT, Claude, and Gemini have never seen your brand mentioned in a press article or review site, you're not in their world model. AI Visibility measures your pre-existing presence in the sources AI models draw on when generating recommendations.
Sub-signals tested
Mentioned in AI model outputs (Claude, ChatGPT, Gemini)
Does asking AI assistants about your category surface your brand?
Press / media citations
Has your brand been mentioned in news outlets or trade publications that AI models train on?
Review platform presence (Trustpilot, G2, Capterra)
Do major review aggregators have an active listing for your brand?
Industry directory listings
Are you listed in category-relevant directories that AI models cite?
Wikipedia or Wikidata presence
For larger brands: does your entity appear in Wikidata with accurate claims?
Perplexity / AI search citation rate
When Perplexity answers category questions, do you appear in source citations?
Quick fix: Build citations: contribute to industry publications, get listed on review platforms, and earn press coverage. Create content that earns links from AI-indexed sources.
Competitive Readiness
/10 ptsCan agents justify recommending you over rivals?
An agent doesn't just need to find and understand you — it needs to argue for you. When a buyer asks which brand to buy, an agent runs a comparison. Competitive readiness measures whether you've given agents the proof signals they need: AggregateRating data, a differentiator statement, and explicit comparison content. Without these, agents default to recommending the best-documented competitor.
Sub-signals tested
AggregateRating schema with count and value
Do your product pages declare a valid AggregateRating with reviewCount and ratingValue?
Clear differentiator statement
Is there an explicit, crawlable statement of what makes you different from competitors?
Comparison page or comparison content
Do you have a page comparing your products to named competitors?
Trust signals (certifications, guarantees, press logos)
Are trust badges and proof points machine-readable and not just images?
Price positioning signal
Is your pricing tier (budget / mid / premium) clear to an agent parsing your site?
Quick fix: Add AggregateRating schema to product pages. Write a clear 'Why us?' page with crawlable comparison content. Ensure trust signals are text-based, not image-only.
Agent Governance
/8 ptsHave you told agents what they're allowed to do?
As agents move from research to transaction, they need explicit permission from site owners. /AGENTS.md is the emerging standard: a document declaring which agents are permitted to act on your site, what actions they can take, and how disputes are handled. Without it, well-designed agents treat your site as a no-autonomous-transaction zone — they'll research but won't buy.
Sub-signals tested
/AGENTS.md published and valid
Does your site publish an /AGENTS.md declaring agent permissions and allowed actions?
Agent purchase workflow documented
Is there a documented process for how agents initiate, confirm, and complete purchases?
Data access policy for agents
Have you declared what personal data agents can access or store on behalf of buyers?
Dispute and cancellation process
Is there a machine-readable process for agents to cancel or modify an order?
Agent authentication method
Is there an OAuth or API-key based method for agent identity verification?
Quick fix: Publish /AGENTS.md at your site root. See agents.do or llmstxt.org for templates. Document which agent actions are permitted and how purchases are authorised.
How scoring works
Each domain is crawled using a headless browser that captures raw HTML, JavaScript-rendered content, structured data, robots.txt, and accessibility files including /llms.txt and /AGENTS.md.
Technical signals (schema presence, robots rules, token counts) are scored deterministically. Qualitative dimensions (content quality, actionability depth, competitive differentiation) are assessed using Claude AI with a structured rubric that returns consistent, auditable JSON scores.
Entity consistency is checked by comparing business facts scraped from Google's knowledge panel, LinkedIn public pages, and Yelp against the domain's own website content and schema declarations.
AI visibility is tested by running a standard battery of prompts against live Claude and ChatGPT instances, recording mention rate, citation rate, and whether the brand is recommended or excluded.
Human approval likelihood is a directional model — it reflects the relationship between score completeness, evidence quality, and an agent's ability to build a verifiable recommendation buyers trust. It is not a guaranteed conversion rate.
See where your site falls across all 7 dimensions.
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