The agent successfully gathered enough information to explain Fleet's core purpose, positioning, pricing, and differentiation, though it had to work around JavaScript-rendered content and incomplete pricing mechanics. The site's main pages (homepage, pricing, docs, comparison, audience-specific landing pages) were reachable and cited, but the agent could not fully extract detailed feature information or clarify open questions about deployment architecture and Claude Code coupling due to client-side rendering limitations.

• ›Steps [1], [3], [5], [10], [18], [20] all returned 200 and were explicitly cited as sources in the final response. Each addressed a core question: homepage (what it does), pricing page (cost structure), docs (workflows), comparison page (vs. alternatives), and audience pages (who it's for).
• ›The agent had to rely on prior knowledge (11%) and web search results (33%) to fill gaps that the site's HTML didn't expose—pricing mechanics, GitHub repo status, and exact feature details were not clearly machine-readable in the fetched responses, despite being present on the site.
• ›Navigation was straightforward (direct /pricing, /docs, /for-leaders guesses worked; /features returned 404). The site is a Next.js app with JavaScript-rendered content, meaning full details are in interactive components the agent couldn't parse—this forced reliance on search engine snippets and prior knowledge rather than direct site content.
• ›The agent correctly identified two key positioning gaps: (1) unclear whether Fleet is actually open source (GitHub repo not found), and (2) vague billing model (charged per 'workflow start' but definition unclear). These were honest gaps, not knowledge failures.
• ›The /compare page exists and was fetched but the agent could not read its detailed comparison table due to rendering, yet still delivered a coherent competitive analysis by inferring from homepage and audience positioning.