The agent successfully assembled a comprehensive explanation of Melies by combining direct website fetches with web search results, since the site's JavaScript-heavy rendering made HTML responses largely unreadable. It extracted pricing tiers from JSON payloads, identified core features (screenwriting, video generation, voice, character consistency, timeline editor) from search results and cited pages, and positioned Melies as an integrated alternative to single-model competitors. The site proved only partially navigable for machine reading: pricing was buried in structured _payload.json files, feature details remained inaccessible in the HTML, and critical information about credit costs and trial availability had to be sourced from external search results rather than the site itself.

• ›Pricing data was successfully extracted from _payload.json (steps 7, 8, 24), but only because the agent probed for machine-readable artifacts; the rendered HTML pages did not contain readable pricing information.
• ›Feature descriptions and differentiators came primarily from web search results and cited comparison pages (steps 20, 21) rather than from the homepage or feature pages, indicating those pages are likely JavaScript-rendered and not machine-readable in raw HTML form.
• ›The agent had to note outdated pricing plan names (Indie, Filmmaker, Studio vs. current Starter, Pro, Max) found in search results, suggesting the site underwent restructuring but external sources were not updated—a data consistency problem.
• ›Critical details like credit costs per model, free trial availability, and detailed entitlements were partially inaccessible; the agent relied on search results citing the credits-pricing documentation rather than directly readable content.
• ›The site is structured as a Nuxt application with prerendered pages, but the HTML responses were truncated and JavaScript-dependent, forcing the agent to use web search (29% of fetches) and prior knowledge (12%) to backfill the gaps left by direct fetches (59%).