The agent gathered a partial but coherent understanding of Kortify by relying almost entirely on web search results and prior knowledge, since the website itself was largely inaccessible due to client-side rendering and missing documentation pages. It successfully identified what Kortify does (meal planning built around supermarket deals), who it targets (Dutch budget shoppers), and how it differs from competitors (Spaartje, Trolix, HelloFresh), but could not definitively determine the pricing model or confirm product availability because key pages (pricing, features, about) returned 404s and no app store presence was verified.

• ›The homepage and all attempted direct page fetches ([1], [2], [4], [5], [6], [13], [14], [23]) returned only minimal HTML scaffolding with no rendered content—a Next.js app that does not expose its content through static HTTP responses. The agent could not extract answers directly from the site.
• ›All substantive product information came from web search results ([8], [11], [12], [19], [20]) and prior knowledge. Search snippets from Google revealed the value proposition ('Aanbiedingen + merken vergelijken voor slim besparen,' 'Bespaar tot 30% op Boodschappen,' price comparison across 10+ Dutch supermarket chains), and competitor names (Trolix, Spaartje). The agent synthesized these into a coherent narrative without ever accessing rendered Kortify content.
• ›Critical gaps remained unfilled: pricing model, app store presence, and step-by-step user flow were all marked as missing or unconfirmed despite 27 search attempts and fetch steps. The agent had to acknowledge these limitations explicitly in its response, correctly marking the outcome as 'partial.'
• ›The site's infrastructure (Next.js SPA, JavaScript-dependent rendering) is inherently hostile to static content extraction. No structured data (schema.org, JSON-LD), no plain-text fallback, and no published docs meant the agent could only learn via search engine indexing and external mentions, not direct site inspection.