The agent partially completed the task by assembling a coherent overview of Dr. Boomgaarden's practice, but worked around significant site limitations. The website is heavily JavaScript-dependent and does not expose pricing, service details, or differentiators in a machine-readable way; the agent relied on web search results and prior knowledge to fill critical gaps. Specific pricing remained unavailable despite a dedicated /investment page, leaving a key part of the task incomplete.

• ›The homepage (step 1) and services page (step 4) returned only JavaScript framework code with no rendered content, making the site opaque to structured data extraction. The agent could not access what these pages actually displayed.
• ›The /investment page (step 11) also returned only framework HTML—pricing information exists somewhere on the site but was not fetchable in the response, forcing the agent to disclose this as a data gap rather than report a concrete fee schedule.
• ›Web search results (steps 9, 13, 19) were the primary source of actionable information: they surfaced the existence of the /investment page URL, confirmed her DABCL credential, and revealed her background via a Bastyr University news article. The agent used search as a workaround to navigate a site that doesn't expose its own structure.
• ›The FAQ (step 16) and blog post (step 17) also returned only framework HTML, yielding no usable content despite being cited as sources. The agent cited them to show diligence but acknowledged it could not actually read their content.
• ›The agent correctly identified that the practice operates on a concierge/transparent-pricing model outside insurance, accepts HSA/FSA, offers payment plans, and focuses on cardiovascular/metabolic conditions—but this was reconstructed from search snippets and prior knowledge, not from the website itself.
• ›The site is unusable for machine-readable discovery: key pages (services, pricing, FAQ, about, blog) are client-side rendered and return no content in the response body. The agent had to rely on 56% prior knowledge and 33% web search to compensate.