The agent assembled a comprehensive explanation of TD Bank's services, target market, pricing, and competitive positioning by combining sparse direct site content with extensive web search and external sources. The td.com site itself was poorly navigable for automated discovery—it relies on heavy JavaScript rendering and does not surface detailed product information, fee structures, or differentiation in machine-readable form—forcing the agent to source ~75% of its answer from web search results and third-party financial reviews rather than from td.com's own pages.

• ›Steps [1], [4], [5] fetched td.com directly but returned only truncated HTML with minimal substantive content; the site's heavy JavaScript dependency meant static scraped content did not include product details, pricing tiers, APY rates, or account benefits.
• ›Steps [8]–[15] (web searches and external site fetches) yielded the overwhelming majority of the final answer: fee schedules came from search results and third-party sites (Wealthvieu, Yahoo Finance); competitive positioning came from analyst sites (PitchGrade, finder.com, Lendstart); customer segment data came from business analysis sites; and branch/asset cap information came from company news and Wikipedia. Only step [13] (td.com's own checking-account-fees page) provided directly cited site content, but even that was truncated.
• ›The agent explicitly called out that td.com relies on dynamic content rendering, making it difficult to scrape product details and tier benefits—the site is effectively opaque to automated discovery. The agent worked around this by relying on prior knowledge (25%), web search (25%), and prior artifact (50%), indicating the site does not publish its own answer in a form ready for agent consumption.