The agent successfully assembled a comprehensive explanation of Skymetrics' business model, target audience, pricing, and competitive positioning—but had to work around the site's own pages, which failed to load their full content. It relied on third-party app directories, SaaS review sites, and web search snippets to construct the answer, supplemented by prior knowledge about the competitive landscape. The site is poorly machine-readable due to heavy JavaScript rendering and truncated HTML responses.

• ›Steps [1]–[7] fetched skymetrics.co directly but all responses truncated at ~4400 chars, rendering the primary site inaccessible. The agent pivoted to external sources after recognizing this pattern.
• ›Steps [9]–[14] succeeded by using web search and third-party aggregators: Shopify App Store listing [13] provided official product description and 14-day free trial info; SaaS review sites [11, 12] surfaced pricing tiers ($99, $299, $549/month) that the agent cited; Skymetrics' own blog articles [15, 16] on competitive comparisons were fetched but also truncated, forcing reliance on search snippet text instead.
• ›The agent cited seven sources in its final response, but only 2–3 were directly readable: most intelligence came from search results (step [9, 10, 14] snippets), meta descriptions, and third-party restatements rather than Skymetrics' own content. The site publishes pricing, feature names, and positioning language but does not surface them in agent-friendly, complete HTML.
• ›The agent explicitly flagged three content gaps: AI Agent task specifics remain vague; feature-tier mapping unclear ('what does 250 AI messages mean?'); no company background or documentation links found. These reflect genuine site omissions, not retrieval failure.
• ›Prior knowledge contributed 18% of the fetch sourcing (competitive positioning vs. Triple Whale, Inventory Planner); web search 45%; and reuse of earlier artifacts 36%—indicating heavy reliance on external context to fill gaps left by the truncated primary site.