The agent successfully assembled a comprehensive explanation of Goodfit.so by combining web search results with prior knowledge, since the website itself is heavily client-side rendered and does not surface content in a machine-readable form. The agent identified Goodfit as an AI-powered hiring platform with per-candidate pricing, multilingual voice interview capabilities, and positioning as a HireVue/Harver alternative, but had to rely entirely on search snippets and external sources rather than the site's own pages to construct the answer.

• ›All substantive content came from web search results (steps 6, 7, 9, 10, 11, 12), not from direct site fetches (steps 1, 3, 4, 8). The search snippets contained positioning, pricing, features, and competitive comparisons; the site fetches returned only HTML skeletons without rendered content.
• ›The agent cited 8 sources in its final response, but only 2 were direct site pages (homepage and pricing); the other 6 were comparison pages, guides, and external news articles discovered via search. This indicates the site does not publish core information in a consistently accessible form.
• ›Key differentiators (per-assessment pricing at $5, WhatsApp-first delivery, 14-language support, no per-seat fees) were inferred from search results and comparison pages, not stated clearly on the homepage. The agent had to assemble these from fragments across multiple search queries.
• ›The agent explicitly called out 7 items that were confusing or hard to find (company background, actual feature details, enterprise features, success metrics, free trial mechanics, integration ecosystem, customer base specifics), indicating the site publishes positioning but obscures execution details.
• ›Client-side rendering (noted in step 5) prevented the agent from extracting content directly from /pricing, /about, and /compare/* pages despite HTTP 200 responses. The agent worked around this via search-driven discovery rather than site navigation.