AI agents like OpenAI Operator, Browser Use, and Manus navigate via the Accessibility Tree — not screenshots. Your site needs semantic HTML, JSON-LD structured data, and cryptographic auth to be visible in the agentic web.
Vision costs too many tokens. DOM parsing exceeds context windows. The Accessibility Tree distills your page into a pure semantic map agents can actually reason over.
<div class="sc-AxjAm bqwSGR">
<div class="sc-AxirZ gKbhqU">
<div class="css-1dbjc4n r-1awozwy">
<div role="button" tabindex="0"
class="css-18t94o4 css-1dbjc4n
r-42olwf r-sdzlij r-1phboty
r-rs99b7 r-2yi16 r-1qi8awa">
<!-- What does this do? -->
<!-- 120KB of nested divs -->
<!-- Exceeds LLM context -->
</div>
</div>
</div>
</div>
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The AXTree costs a fraction of the tokens. Use semantic <button>, <nav>, <article> — not styled <div> tags.
Vision burns tokens. DOM parsing exceeds context windows. The Accessibility Tree is the only perception mode that delivers semantic clarity at minimal cost.
Web accessibility compliance is no longer merely a regulatory issue — it is the literal API through which AI agents navigate the internet.
Make your site agent-ready →Robots.txt is an honor system. User-Agent strings are trivially spoofed. Residential proxies defeat IP blocklists. The only reliable verification is cryptographic.
The complete architecture for making your site visible, navigable, and actionable by AI agents.
Semantic HTML builds a pristine Accessibility Tree. Use native <button>, <nav>, <article> with ARIA roles — the literal API agents navigate through.
Structured data in the <head> eliminates AI guessing. Organization, Product, FAQPage, and SpeakableSpecification schemas anchor your brand in knowledge graphs.
A Markdown map at your site root. Agents ingest your entire architecture in one request — bypassing HTML bloat, ads, and navigation noise.
Declare what actions agents can take. Declarative API for forms, Imperative API for complex JS workflows. Deterministic tool execution, zero guessing.
Legitimate agents navigate semantically and prove identity with Ed25519 signatures. Mass scrapers hit empty DOM, poisoned payloads, and WAF interdiction.
Fragment data behind IntersectionObserver triggers. No full tables in static HTML — require scroll, click, and tab interactions before content renders in the DOM.
Consumer agents (OpenAI Operator, Browser Use) navigate sequentially and trigger observers naturally. Bulk scrapers fetch raw HTML and find empty containers.
Track session traversal depth, navigation timing, and request cadence. Legitimate agents visit 3–7 pages at human-like intervals. Scrapers attempt hundreds in milliseconds.
ML-driven baseline analysis detects behavioral anomalies — residential proxy rotation, randomized fingerprints, and unnatural mouse movement patterns.
Hidden URLs with zero AXTree semantic value — invisible to humans and well-behaved agents. Any programmatic interaction provides deterministic proof of scraping intent.
Flagged sessions face computationally expensive challenges. LLMs are explicitly trained not to solve CAPTCHAs — the scraper workflow halts completely.
Verified agents present Ed25519 HTTP Message Signatures (RFC 9421). The server retrieves JWKS public keys and validates identity with mathematical certainty — no IP or User-Agent needed.
Each verified bot carries a Signature-Agent Card with rate expectations, purpose declarations, and contact metadata. WAF rules auto-configure per-agent rate limits.
Verified live-retrieval agents (ChatGPT-User, Claude-User, Perplexity-User) — triggered by human prompts, cryptographically signed, semantically navigating.
Mass-training crawlers (GPTBot, ClaudeBot) and unauthorized scrapers. Unverified entities trigger adaptive MFA authentication before any high-risk action proceeds.
AXTree optimization, JSON-LD, llms.txt, WebMCP, and Ed25519 verification — deployed in one config. Drop your email for early access.
Free during beta • No credit card required