The agent assembled a partial but coherent explanation of Recost by relying primarily on web search results and prior knowledge, since the homepage itself is a JavaScript-rendered React app that did not return meaningful content. The agent successfully identified what Recost does (API cost monitoring with AST-based code analysis), who it's for (LLM/AI developers), and how it differs from competitors, but could not find pricing information or confirm the official GitHub repository—gaps the agent explicitly flagged. The site's lack of accessible public documentation, pricing page, or searchable structured data forced heavy reliance on indirect sources (Product Hunt mentions, blog comparisons) rather than first-party information.

• ›Step [1] returned only an HTML skeleton with no rendered content; the React app did not hydrate during fetch, making the homepage non-navigable. The agent pivoted entirely to web search.
• ›Steps [3], [5], [8], [11] were search results that returned link titles and snippets mentioning Recost alongside competitor tools and launch references (Product Hunt), allowing the agent to infer positioning and audience. However, none of these searches returned direct access to a pricing page, docs site, or official repo.
• ›Step [15] (attempt to fetch docs.recost.dev/) failed with a connection error, confirming the absence of publicly accessible documentation infrastructure.
• ›The agent's final answer was assembled from prior knowledge (what Recost is and how it works) combined with scattered search snippets positioning it against competitors. No single fetch or search result contained a complete answer; the agent synthesized across multiple indirect signals.
• ›Critical gaps remain: pricing is not published on any accessible page; the GitHub repo is referenced as 'building in the open' but no official repo URL was found; installation/SDK instructions are not public. The site is effectively invisible to agent discovery beyond its homepage URL and Product Hunt mention.