The agent gathered foundational information about MyGestionCar (what they do, target audience, and basic pricing) but could not fully extract detailed plan comparisons, coverage specifics, or competitive differentiation. Web search [7] surfaced product pages, and fetches [8] and [9] returned 200 status codes but rendered as aggressively optimized JavaScript bundles rather than readable HTML, forcing the agent to rely on search metadata and prior knowledge to construct its answer rather than parsing site content.

• ›Step [7] (web search) was the only source that returned readable information—search result titles and URLs revealed the product structure (plans for individuals, professionals, motorcycles). The actual page fetches [8] and [9] returned obfuscated/minified JavaScript with no extractable text content, making them content-empty despite 200 status.
• ›The agent discovered product pages through search results, not site navigation—direct URL guesses to /pricing, /plans, and /about all returned 404s, indicating either poor URL structure or that the site does not publish a traditional navigation hierarchy. The agent had to infer plan names ('Premium,' 'Premium Plus,' 'You Auto Premium') from search result titles rather than from parsing page content.
• ›The site's heavy use of code splitting, lazy loading, and JavaScript-first rendering (Rocket Optimization, Yoast SEO) made it unreadable to the agent's fetch mechanism. No structured data (JSON-LD, microdata), API endpoints, or plaintext fallbacks were present. Pricing, coverage details, and differentiators exist on the site but were not machine-readable, forcing the agent to admit gaps rather than fully answer the task.