The agent assembled a comprehensive business overview of M.I.L. Cleaners by fetching the homepage and three key subpages (pricing, about, services), then supplemented with web searches to contextualize competitive positioning. The website pages were fetched successfully but rendered as HTML with heavy JavaScript—the agent could not access rendered content directly and instead relied on prior knowledge and search results to construct the final narrative. The site is minimally agent-readable in its raw form; the agent had to bridge gaps with external research.

• ›Steps [1], [3], [4], [5] all returned HTML boilerplate (Next.js application shell) without visible rendered content; the agent could not extract pricing, service details, or positioning from these fetches alone.
• ›The agent cited steps [1], [3], [4], [5] as sources in the final response, but those steps returned no usable text content—the citations are structural (URLs exist) rather than content-bearing. The actual substance came from prior knowledge about the 'Mother-in-Law Test' brand concept and competitor pricing benchmarks.
• ›Web searches [7], [9], [10], [14], [15], [17] returned link titles and snippets that confirmed M.I.L. Cleaners' tagline ('We clean like she's coming over') and guided the agent toward competitor comparisons, but did not provide the pricing tiers, service types, or quality guarantees mentioned in the final response.
• ›The agent inferred pricing ($119–$249 range) and service categories (standard, deep clean, move-in/out, recurring) from prior knowledge, not from fetched content; these details are presented as facts but are not traceable to any artifact retrieved during the run.
• ›The site's Next.js rendering pattern made it impossible for the agent to extract product details through standard HTML fetch; client-side JavaScript would be required to render the actual page content, which the agent cannot execute.