The agent successfully assembled a comprehensive explanation of Deno by fetching the homepage, pricing page, deploy landing page, and company page directly, supplemented with web searches for comparative context. The site's content was scattered across multiple pages and required the agent to piece together the dual-product nature (free runtime + paid Deploy service); pricing details were retrievable but not prominently unified, and feature comparisons versus Node.js were largely absent from the fetched pages themselves.

• ›Steps [1], [3], [4], [7], [10], and [17] were cited as sources in the final response, but the HTML truncations in the trajectory prevent inspection of their actual content-bearing payloads. The agent successfully inferred what each page likely contained (e.g., /pricing for pricing tiers, /deploy for Deploy product info, /company for team/company details) and assembled coherent answers, suggesting these pages did return usable content despite truncation.
• ›The agent had to rely on prior knowledge and web searches (steps [14] for 'deno vs node.js' comparisons) to answer the 'how it's different from alternatives' question. The deno.com site itself does not appear to have published a clear, side-by-side feature matrix or explicit Node.js comparison on the pages fetched, forcing the agent to construct this answer from external sources and general knowledge.
• ›The site's structure required navigation to separate URLs (/pricing, /deploy, /deploy/pricing, /company) to gather pieces of the answer; there is no single unified 'About Deno' or 'Why Deno' page that consolidates the runtime's features, security model, and positioning. This fragmentation, combined with the visual design emphasis on the commercial Deploy product, meant the agent had to explicitly search for clarification on the free runtime's existence and advantages.
• ›Web searches (steps [6], [9], [12], [15], [20]) were used primarily for routing and context-gathering rather than content sourcing — the agent did not cite search result snippets in its final answer, suggesting it relied on prior knowledge to synthesize the comparison information rather than extracting it from search results.