The agent successfully assembled a comprehensive explanation of RevenueCat's core offering, target audience, pricing model, and competitive positioning by combining direct site fetches with prior knowledge. However, the site itself was not fully agent-readable—key pages like pricing and blog rely on heavy JavaScript rendering that truncated in the HTML responses, forcing the agent to work around inaccessible content and rely on prior knowledge (71% of fetches) and external search results to fill gaps. The agent delivered a satisfactory answer despite friction from the site's technical architecture.

• ›Homepage [1], pricing [2], about [3], docs [7], and blog [11] were all fetched but returned heavily truncated JavaScript-rendered HTML, making them unreadable to the agent. The agent could not actually see the pricing table, blog post details, or feature comparisons from the site itself.
• ›The agent relied primarily on prior knowledge (71%) rather than fresh content from the site. The three web searches [6, 9, 10, 12, 13] provided external comparisons and context, but were not strictly necessary for the core answer—they supplemented what the agent already knew about RevenueCat.
• ›RevenueCat's documentation site [7] was the only content-bearing fetch that returned readable HTML (Docusaurus-based), but the agent did not follow deeper into the docs to extract specific feature lists or use-case examples. The site publishes extensive documentation but the agent did not cite any specific doc pages.
• ›The agent explicitly called out confusion around 'RevenueCat Billing' as a separate product, and noted that the pricing page structure was opaque due to JavaScript rendering. This reflects genuine gaps in what the site made accessible.
• ›None of the main site fetches [1, 2, 3, 11] were explicitly cited in the final response's sources—only external comparisons and the docs homepage were referenced, indicating the agent composed the answer primarily from prior knowledge rather than fresh extraction.