The agent successfully assembled a comprehensive explanation of Datum.net's purpose, pricing, target audience, and differentiation, but had to rely heavily on web search and prior knowledge rather than directly accessible website content. The site's compressed HTML and limited semantic structure made direct fetches unhelpful; the agent pieced together a coherent picture from search results, press releases, and documentation snippets, ultimately delivering a well-organized answer that satisfied the exploratory intent.

• ›Direct fetches of datum.net pages [1, 3, 5, 6, 10, 11, 16] returned heavily Brotli-compressed HTML that was truncated and uninformative; the agent abandoned this approach and pivoted to web search after step [6], indicating the site is not agent-readable in its native form.
• ›Search results [8, 13, 18, 19, 21, 24, 28, 30] proved content-bearing, surfacing press releases (AlleyWatch funding story), press partnerships (DataBank), GitHub repository, and product positioning language ('open network cloud', 'for AI companies'). These were not discoverable via the site's own structure but via external indexing.
• ›The agent explicitly identified knowledge gaps in the final response—usage limits, compute specifics, Scaler tier feature timelines, Provider tier pricing, and competitive positioning—indicating that even after exhaustive search, Datum's marketing site does not clearly document key product and pricing details.
• ›The agent cited 8 sources in the final response, but only 4 were from datum.net proper (features, pricing, essentials, docs overview); the other 4 were external press coverage and GitHub, showing the agent relied on third-party articulation of Datum's value proposition rather than the company's own published materials.
• ›Prior knowledge contributed 43% of the fetch sourcing strategy, suggesting the agent leaned on its training data about infrastructure platforms, CDNs, and AI workloads to fill gaps left by the website itself.