The agent successfully assembled a comprehensive overview of SparrowDesk by combining web search results with fetches from the main marketing site (www.sparrowdesk.com), since the support subdomain (support.sparrowdesk.com) proved to be a help center rather than a product information resource. The site's marketing pages and blog comparisons were navigable and information-rich, but key details like Enterprise pricing and add-on costs remained opaque, requiring the agent to work around incomplete transparency.

• ›The support.sparrowdesk.com domain was fetched first but contained only help center documentation; all substantive product, pricing, and differentiation information came from www.sparrowdesk.com (main site, pricing page, blog comparison articles) and external reviews (Capterra, SaaSTrac). The support subdomain was not useful for this discovery task.
• ›Web searches (steps [4], [7], [8], [10]) were more productive than direct site navigation, returning both internal blog content (SparrowDesk comparisons vs. Freshdesk, Zendesk, Intercom) and third-party reviews. The agent relied heavily on prior knowledge and search results (80% combined) rather than native site structure.
• ›Pricing information was partially transparent: the agent found Free, Starter ($16/user/month), Professional ($49/user/month), and Enterprise ($89/user/month) tiers on the pricing page, but the page did not clearly specify Enterprise pricing limits, add-on costs, or what features differentiate each tier—gaps the agent noted explicitly.
• ›The site's marketing positioning and blog content clearly articulated differentiation (AI autonomy via 'Zoona,' omnichannel unified inbox, 60% auto-resolution claim), but specific implementation details (onboarding timeline, training speed, add-on pricing) were not discoverable through either direct site fetches or the agent's search attempts.
• ›The agent was unable to extract structured, machine-readable content from the HTML responses (all fetches returned truncated HTML); final answer relied on piecing together information from search result snippets and prior knowledge rather than parsing live site content.