The agent assembled a comprehensive explanation of Green 100 by combining the homepage, About page, sign-up page, and multiple web search results. The site itself was minimally navigable—most subpages (pricing, how-it-works, FAQ) returned 404s—forcing the agent to rely heavily on web search and prior knowledge to fill gaps, especially around pricing details and differentiators. Despite this friction, the agent succeeded in explaining what Green 100 does, who it serves, its free-to-premium model, and how it differs from alternatives, though premium pricing remained opaque.

• ›Steps [1] and [10] (homepage fetches) returned the core mission statement and meta description but lacked operational detail; the full content was truncated in the response, limiting what could be extracted directly.
• ›Steps [3], [4], [5], [7], [8] all returned 404s—the site has no dedicated /pricing, /how-it-works, /faq, or /for-smes pages, forcing discovery to rely on search results and external press coverage rather than the site's own information architecture.
• ›Step [15] (About page) was the most structured content-bearing fetch, providing context on the 'Queen Bee' model and TR 149:2026 framework, but still lacked granular operational details.
• ›Step [16] (Sign-up page) was cited in sources but appears to have been a solution-provider form rather than end-user documentation.
• ›Steps [12], [13], [18], [21], [24] (web searches) were critical to answering the task—search results contained press releases, news articles, and regulatory announcements that the homepage itself did not synthesize, particularly around pricing (step [24] found the 'zero-cost 10-min' framing) and the 'Queen Bee' mechanism.
• ›The agent explicitly acknowledged in its final response that premium pricing was not clearly published on the site and would require contacting sales (green100@gprnt.ai)—a gap it worked around via web search.
• ›The site is not agent-ready for discovery-evaluation tasks: it lacks structured data (schema.org), does not publish pricing or feature tables in machine-readable form, and relies on external press coverage and informal news sites to clarify its value proposition.
• ›44% of fetches came from 'previous_artifact' (likely cached search results or prior runs), 33% from prior knowledge (direct guesses at URLs), and only 22% from fresh web search—indicating the agent had to compensate for a sparse site by relying on cached context.