The agent successfully built a comprehensive understanding of MyArtBroker by fetching the homepage, how-it-works page, buy/sell pages, team page, and a competitive analysis guide. The site was reasonably navigable for discovery, with clear main sections and intuitive URL structure (/buy, /sell, /how-it-works), though pricing information required inference from page content rather than a dedicated pricing page. The agent assembled a coherent business model explanation from fragments across multiple pages, supplemented by prior knowledge about art market fees.

• ›Step [1] (homepage) provided schema.org structured data and meta descriptions that hinted at the value proposition (0% fees, private sales), but the full business model required visiting multiple pages.
• ›Steps [5], [7], [8] (how-it-works, sell, buy pages) directly answered the core task questions: what they do (private sales platform), who it's for (collectors and sellers), and the fee structure (0% for sellers, ~18% for buyers implied from page titles). These pages were text-rich and semantically clear.
• ›Step [16] (auction vs. private sale guide) provided competitive framing, but the agent had to rely on prior knowledge of traditional auction house fees (20-28% buyer's premiums) to contextualize the difference—this comparison was not explicitly stated on the fetched pages.
• ›The site lacks a dedicated /pricing page (steps [3], [4] returned 404s), forcing the agent to extract pricing from page copy and infer buyer fees from the sell/buy page titles and context rather than from a single authoritative source.
• ›Step [12] (our-people page) added credibility signals (team expertise, blue-chip market background) but was not strictly necessary for answering the core task; it was exploratory.
• ›The agent did not fetch the MyPortfolio pages it searched for in steps [20], instead relying on search result snippets and prior knowledge to describe the feature—suggesting the feature exists but was not directly verified in the trajectory.
• ›The site is moderately agent-ready: clear navigation, accessible URLs, and semantic HTML/schema.org markup made discovery straightforward, but the absence of a centralized pricing or feature matrix page required multi-page assembly of the answer.