Agent successfully assembled a comprehensive explanation of TypeSafe's Jev by combining documentation pages with web search results. The site's docs were sparse but sufficient for core concepts; critical comparative and business context came from external sources the agent had to discover via search.

• ›Homepage (step [1]) provided clear conceptual foundation (System One, three primitives: Choice/Score/Noul) but lacked pricing, audience, and competitive positioning — forcing web search dependency.
• ›Models page (step [4]) supplied concrete pricing ($42/billion input tokens, output free) and rate limits, but was discovered via search referral chain, not homepage navigation; docs structure was fragmented.
• ›Web search (step [6]) was essential: it surfaced comparison claims (440× cheaper, 190× faster), use cases, and differentiation points that docs.typesafe.ai did not contain, indicating the docs site focuses on technical API reference rather than positioning or business context.
• ›Blog post (step [7]) addressed target audience and use cases directly, but was only reachable via external search, not linked from docs homepage, showing a disconnect between docs, marketing, and discovery.
• ›Agent had to infer several answers (e.g., 'API-only deployment', 'only one model exists', 'no alternative options') from absence of features in docs rather than explicit statements — typical of incomplete product documentation.