The agent assembled a partial but usable understanding of Notat.ai by relying almost entirely on web search results and prior knowledge, since the site's client-side rendering prevented direct content extraction. The agent successfully explained what Notat does (ambient AI medical scribe), who it's for (organized healthcare clinics), and key differentiators (FactsContext engine), but could not retrieve exact pricing tiers or complete feature comparisons because the site's interactive pages did not load their content in the fetched HTML.

• ›Steps [1–5, 9, 19] fetched the site directly but returned only skeleton HTML with no visible content — all substantive information came from web search results (steps [7, 8, 10, 12, 13, 14, 18, 21, 22]), which contained snippets about Notat's positioning, competitors, app availability, and blog post titles.
• ›The agent correctly identified that pricing and feature details were inaccessible ('client-side rendered and I couldn't access the specific price details') and worked around this by citing indirect evidence (metadata, trial period mentions, competitor comparisons) rather than inventing answers — showing good epistemic restraint.
• ›The site's navigation structure and content were discoverable only through web search and the App Store; the site itself served as a poor primary source for its own value proposition. The agent had to piece together 'what Notat is' from blog post titles, About page existence, and comparisons published by third parties.
• ›The FactsContext™ differentiator was mentioned in a search query the agent constructed (step [20]) but never resolved in the retrieved results; the agent noted this gap in the 'confusing/couldn't find' section, showing awareness of incomplete information.
• ›Prior knowledge contributed 14% of the fetches, suggesting the agent may have drawn on background understanding of the medical AI scribe category to frame its explanation and comparisons with Freed, Abridge, and Doximity.