The agent assembled a coherent explanation of Elease's purpose, audience, and positioning, but had to work around a JavaScript-heavy site that blocked access to static content and relied heavily on prior knowledge and web search to fill gaps. The site's core value proposition (EV leasing comparison platform) was inferable from URLs and metadata, but pricing model, business logic, and differentiators required external web search and educated guessing rather than being explicitly published.

• ›Step [2] (homepage) and step [9] (/om-os) returned only HTML skeletons with dynamic content markers; the actual text explaining who Elease is, what they do, and how they work was not in the fetched HTML, indicating heavy reliance on client-side JavaScript rendering that made the site opaque to static fetching.
• ›Steps [15], [20], [21] were web searches that returned metadata snippets (e.g., 'Danmarks uafhængige elbil-leasingplatform' from step [21]) that the agent used to infer Elease's independence claim and positioning; this information was NOT on the Elease site itself but on external references.
• ›Step [18] (calculator page) also returned only an HTML skeleton with no pricing examples or visible calculation logic, forcing the agent to state that pricing data could not be found and infer the business model (affiliate commissions) based on prior knowledge rather than explicit disclosure.
• ›The agent could infer 'what they do' from URL patterns (/elbil-leasing, /beregner) and search result titles, but could not access or cite the About Us page content, the blog, the calculator interface, or any FAQ—all key resources for a discovery-evaluation task.
• ›Critical gaps acknowledged in the final response: business model, Elease Score explanation, data update frequency, and coverage scope—all of which would normally be documented on a company's public site but were inaccessible via static fetch.