The agent assembled a coherent explanation of Seatbelt's purpose, pricing, and positioning by stitching together fragments from the site itself (homepage, /pricing, /app, /commands pages) and heavy reliance on prior knowledge and web search to fill gaps. The site proved difficult to navigate — it is a Next.js SPA that returned truncated HTML in every fetch, making direct content extraction impossible. The agent succeeded in inferring what Seatbelt does (security scanning for AI-generated code) and its free-forever core model, but could not confirm key details like the specific "seven risk surfaces" or clarify how the $250/$1,500 paid tiers work, and had to resort to external search and prior knowledge to validate its understanding.

• ›Every page fetch (steps [0], [1], [2], [6], [8], [11], [17], [18]) returned identical truncated HTML boilerplate with no rendered text content — the site is client-side rendered and not machine-readable via HTTP fetch alone. The agent could not extract any actual text from the site's pages.
• ›The agent relied heavily on prior knowledge (22%) and web search (11%) to construct its answer because the site's HTML was inaccessible. Step [14] search result pointing to the GitHub project (agentseatbelt) and step [18] search result (getseatbelt.ai/docs) provided the clearest explanations of what Seatbelt does — external validation, not from withseatbelt.com itself.
• ›The site has pages at /pricing, /how-it-works, /get-started, /app, and /commands, but none of them returned navigable content in the fetches. The agent correctly identified that pricing information exists (free scan + $250/$1,500 tiers) but could not explain the boundary between free and paid from site content alone — this came from prior knowledge and inference.
• ›The agent explicitly flagged that the 'seven risk surfaces' are mentioned repeatedly on the site but never itemized or explained — a clear sign of incomplete information architecture or client-side rendering that hides content from non-browser agents.
• ›The site is agent-hostile: no llms.txt, no structured data in fetched HTML, no machine-readable docs endpoint (step [15] hit a 404 on /docs), and no visible pricing table or feature matrix that a crawler could extract.