The agent successfully assembled a coherent overview of AI Eesti's core business, target market, and positioning by combining the llms.txt file (which provided a structured summary) with HTML page fetches. However, the task was only partially satisfied: while the agent clearly explained what they do and who it's for, pricing remained entirely opaque—the site offers no public pricing information, forcing the agent to note this as a critical gap. The site is moderately agent-navigable for discovery but deliberately opaque for pricing.

• ›Step [6] (llms.txt) was the single most valuable resource: it contained structured, machine-readable metadata describing the company's mission, services (AI Audit, Training, Development, AI Pilot), founding date, location, and target audience—exactly the scaffolding the agent needed to build its explanation.
• ›Step [8] (About page) was cited as a source but the response body was truncated in the trajectory, so its actual content-contribution is unclear; the agent relied primarily on prior knowledge and the llms.txt summary rather than parsing the About page itself.
• ›The site publishes no pricing page, no service packages, no cost ranges, and no engagement models—only a Calendly link for consultations. The agent had to explicitly call this out as a missing requirement, making pricing the largest liability for agent-driven discovery.
• ›The site is a Next.js SPA with client-side rendering, making raw HTML fetches difficult to parse for detailed information; the agent relied heavily on the llms.txt file (a well-designed AI-friendly artifact) rather than scraping rendered content.
• ›Services, positioning, and team background were surfaced through the llms.txt artifact and inferred from site structure, not from deep page content extraction—the agent did not retrieve granular service definitions, case studies, or competitor comparisons from the site itself.