The agent successfully assembled a comprehensive explanation of Statsig's core offering, target audience, pricing model, and differentiation from competitors, but had to rely heavily on web search and third-party sources because Statsig's own website is heavily JavaScript-rendered and its direct pages did not return usable content. The agent identified a critical gap: Statsig was acquired by OpenAI in September 2025 and then its assets were transferred to Amplitude in May 2026, creating significant uncertainty about the product's future direction that was not clearly communicated on the website itself.

• ›Direct site fetches (steps [1–10]) returned only HTML boilerplate with truncated responses, making it impossible to extract content about what Statsig does, its pricing, features, or differentiation. The agent had to abandon the site's own pages after initial exploration.
• ›Web search results (steps [12–13, 15–16, 18–20]) proved content-bearing: search result titles and links themselves provided pricing tiers, feature names, competitor comparisons, and company acquisition news. The agent then fetched specific URLs from those search results (steps [14], [17]) to confirm details.
• ›Steps [14] (comparison page) and [17] (OpenAI acquisition blog) were the only direct site fetches that returned usable content, but even these were heavily JavaScript-rendered and required the agent to have already inferred what to look for from web search.
• ›The agent discovered critical information—the OpenAI acquisition (September 2025) and Amplitude's subsequent takeover (May 2026)—entirely through web search (steps [15–16]), not the website. This is major news that fundamentally affects the product's future but is buried in blog posts rather than surfaced prominently in homepage or product positioning.
• ›Pricing details (Free tier, Pro, Enterprise) came from third-party aggregator sites (softwaresuggest.com, vendr.com) referenced in search results, not from Statsig's own /pricing page, which returned truncated HTML.
• ›The site's heavy JavaScript rendering made it hostile to agent navigation: most fetches returned identical boilerplate headers and truncated bodies. This forced the agent to use prior knowledge and web search for 67% of its information sources (vs. the 33% from direct site fetches).