The agent successfully understood Elvy Energy's core business model, pricing structure, target market, and competitive positioning by relying almost entirely on web search results and prior knowledge, as the website itself (fetched in steps 1, 3, 7, 8) returned only truncated Next.js HTML without extractable content. The agent assembled a coherent answer but had to work around a site that was not machine-readable, leaving several important details (eligibility criteria, geographic coverage, early exit terms) unresolved.

• ›Steps 1, 3, 7, 8 fetched the website directly but returned only HTML boilerplate with no substantive content due to Next.js client-side rendering; the agent could not extract pricing, eligibility, or service details from the site itself.
• ›Steps 5, 10, 11, 13, 15 (all web searches) provided the actual substance of the answer: business model details, 15-year fixed pricing, €230/month estimates, competitor comparisons, and expansion plans. These were the only content-bearing steps used to construct the final response.
• ›The agent cited multiple third-party sources (Dealroom, Sifted, TechFundingNews, Junto.Space) in its sources list rather than the domain itself, indicating the site did not serve as the primary information source despite being the query target.
• ›Key gaps identified by the agent—eligibility criteria, exact geographic availability, contract termination terms, and pricing transparency—were not addressed by either the website or the search results, indicating the site does not publish these details in accessible form.
• ›The site's design (modern, Next.js-based) created friction for information extraction; the agent noted this explicitly and had to rely on web search and prior knowledge (50% and 25% respectively per the trajectory metadata) rather than native site content.