The agent successfully answered the task by relying almost entirely on a single machine-readable file (llms.txt) that Sidekick published for LLM consumption. The website itself is poorly optimized for human or agent browsing—most pages return minimal visible content, pricing and comparison information are absent from the HTML, and the sitemap reveals only 4 pages. The agent pieced together a coherent explanation of what Sidekick does, who it's for, pricing details, and differentiation, but this information was only discoverable because Sidekick explicitly prepared it in a structured text format; the website itself would not have supported this task.

• ›Step [17] (llms.txt) was the sole content-bearing source for the final answer. It contained the headline, support statement, hero facts, pricing structure ($30k to builder, $10k fee to Sidekick, 0.1% equity clause), target audiences (builders and leaders), stage requirements ($20M+ raised), time commitment (90 days, half-time), and explicit positioning against accelerators and cohort models.
• ›The agent attempted to fetch the homepage [1], FAQ [8], and other standard pages but received only HTML boilerplate with no readable content extracted—likely a JavaScript-heavy single-page app that does not pre-render text. The agent correctly intuited that a robots.txt and sitemap might exist and found them, which led to the llms.txt discovery.
• ›The website publishes NO pricing page (404 at /pricing [2]), no about page (404 at /about [9]), and no roster/team page (404 at /roster [10]). All discoverable information is in a non-standard artifact (llms.txt) rather than standard web documents. The agent did not rely on web search results despite multiple search queries; those searches returned unrelated products (Sidekick Homes, Sidekick AI money app, Skanska chatbot, etc.).
• ›The agent accurately called out 7 areas of confusion or missing information: unclear website content due to heavy JavaScript, no announced timeline, unclear repeat-match pricing, vague matching criteria, no explicit competitor comparison on the site, geographic ambiguity, and asymmetric stage requirements. These gaps reflect genuine site weaknesses, not agent failure.
• ›The agent's prior knowledge score (67%) was high but not the primary factor—it was used to guess at URL patterns (robots.txt, sitemap.xml, llms.txt) rather than to fill content gaps. The previous artifact contribution (33%) likely refers to synthesis of llms.txt and FAQ content into the structured final answer.