The agent successfully assembled a comprehensive explanation of SCALE by working around the main website's JavaScript-heavy single-page app design (which returned truncated HTML) and instead relying on web search results and documentation fetches. The site's content was distributed across multiple external sources—documentation at docs.scale-lang.com, blog posts, and third-party coverage—requiring the agent to synthesize information across fragments rather than extract it from a cohesive marketing site.

• ›The main website (scale-lang.com/) was not directly useful; it is a Next.js SPA that returns truncated HTML when fetched, making it unreadable to the agent. Direct URL guesses for /pricing, /product, /about, /docs, /features, and /blog all returned 404s.
• ›Web search results (steps 13, 18, 19, 21, 22) were the primary discovery mechanism, surfacing the documentation domain, blog posts, and third-party coverage. Search snippets themselves contained key differentiators (e.g., '6x better performance vs. hipify').
• ›Documentation at docs.scale-lang.com was fetchable and returned structured HTML (steps 15, 24), though the agent noted it 'doesn't render cleanly when fetched directly'—indicating the docs are designed for browser rendering, not agent parsing.
• ›Critical information gaps were acknowledged: specific commercial pricing is unavailable; hardware roadmap and adoption metrics are not public; the 'Comparison to other solutions' documentation page exists but was not fully extracted.
• ›The agent had to rely heavily on prior knowledge (64% of fetches) and web search (27%) rather than on-site content, with only 9% coming from previous artifact/navigation—indicating the website itself does not self-serve product discovery well for agents.