The agent successfully understood ILR Tracker's core offering, pricing model, and competitive positioning by assembling information from the homepage, tools pages, and guides. The site itself had limited explicit structure (404s on /pricing, /features, /about forced guessing), so the agent relied heavily on metadata tags, guide content, and web search to construct a complete picture. Information was scattered across multiple pages rather than centralized.

• ›Step [1] (homepage) provided only metadata hints ('Free ILR calculator and tracker') and required parsing of Next.js boilerplate; actual feature details were not directly accessible in the fetched response.
• ›Steps [7], [8], [13], [17] returned guides and tools pages that contained substantive content about features, cost breakdowns, and competitor comparisons, but these were discovered through guessing URLs and web search rather than explicit site navigation.
• ›The agent could not retrieve pricing from the site itself (all standard paths returned 404); pricing details came entirely from web search results and prior knowledge of the freemium model.
• ›Site structure is SPA-based with truncated HTML responses in fetches, making it difficult to parse content-bearing information directly; the agent had to infer available pages from metadata and search results.
• ›Key features like 'Sponsor Monitor', 'community timelines', and the relationship to the 'Borderline' mobile app remained opaque despite multiple fetch attempts, indicating incomplete documentation or feature pages that weren't discoverable.