The agent successfully mapped what soymaryarnaldi.com does, who it serves, and how it positions itself, but could not retrieve the pricing information that was explicitly listed as important to the task. The site's pages loaded and contained structural information about services (UGC content creation, brand collaborations, blog, portfolio, media kit, contact), but actual price numbers were either not rendered in the HTML responses or were placed behind interactive elements the agent could not access. The agent had to resort to web search to fill gaps, yielding only generic UGC industry rates rather than Mary Arnaldi's specific rates.

• ›Steps [1], [10], [11], [13], [14], [15], [17] returned successful 200 responses that contained enough metadata and page structure for the agent to infer the site's purpose (UGC creator, brand collaborations, motherhood/lifestyle niche), but none of these responses contained actual pricing dollar amounts in their HTML payloads.
• ›The site explicitly promises 'clear pricing' and 'clear starting rates' in its own copy (quoted in the agent's final response), yet the numbers were not extractable from the fetched pages—suggesting they are either JavaScript-rendered client-side, behind a modal/fold, or intentionally withheld until contact. This is a critical mismatch between stated transparency and actual availability.
• ›The agent discovered pages through navigation inference and direct URL guessing (prior_knowledge: 9%) rather than being guided by a sitemap or clear navigation links in the fetched HTML. Three 404s ([6], [7], [9]) indicate the site does not expose all expected information architecture (/about, /pricing, /shop returned errors, though /shop is mentioned as existing separately).
• ›Web searches ([19], [21], [23]) added social proof (Instagram follower count, brand collaborations) but did not return Mary Arnaldi's actual rates—only generic UGC industry benchmarks. The agent acknowledged this limitation transparently in the final response.
• ›The agent successfully reconstructed positioning, audience, and service offerings from page metadata and structural cues, but the absence of pricing data made the final answer 'partial' against the explicit task requirement to 'understand pricing.'