The agent assembled a fairly complete understanding of SparrowDesk by combining web search results (which surfaced the homepage tagline '60% automation') with prior knowledge and inferences, rather than by parsing full page content from the site itself. The homepage and pricing page were fetched but returned truncated HTML that did not expose readable content; the agent had to rely on search result snippets and titles to extract core positioning, pricing tiers, and differentiators. The site was navigable in theory but not agent-friendly in practice—key information existed but was not delivered in a form the agent could parse from the fetches.

• ›Steps [16] and [18] (web searches for '60% automation' and competitor comparisons) were the only steps that returned human-readable information in their snippets; the search titles and result metadata answered what the product does, who it's for, and how it compares to Zendesk/Freshdesk.
• ›Direct fetches of sparrowdesk.com/ [1], /pricing [3], and /about-us [12] returned Next.js framework HTML with truncated payloads; the agent could not extract content from these responses and had to work around the inaccessible pages by pivoting to web search.
• ›Pricing details (per-agent-month model, $16–$89 tiers, free plan) came from search result titles and snippets [15], not from a direct fetch of the pricing page itself—indicating the site publishes pricing but the fetch mechanism or caching prevented the agent from reading it.
• ›The agent explicitly flagged gaps it could not close: exact Zoona AI add-on pricing, integration count, enterprise compliance details, and company background—all of which appear to exist on the site but were not accessible through fetches.
• ›The agent cited sources from both sparrowdesk.com (pricing page, about-us, Zendesk alternatives comparison) and external review sites (Capterra), mixing site-native content with inferences drawn from search metadata rather than full-page parsing.