The agent successfully understood the site's purpose, target audience, and service model, but encountered a significant gap: pricing information is not published on the website. The agent relied almost entirely on two machine-readable artifact files (llms.txt and llms-full.txt) that the site explicitly provides for AI systems, supplemented by prior knowledge of legal practice norms. The site is highly agent-ready in identity layer but intentionally opaque on pricing.

• ›Steps [3] and [4] (llms.txt and llms-full.txt) were the only content-bearing steps; they directly answered what the firm does, who it serves, and its business model. All other fetches [1,6,7,8,10,11] returned rendered HTML that the agent could not parse into actionable content.
• ›The site publishes dedicated machine-readable context files (llms.txt, llms-full.txt) specifically designed for AI agents, making identity layer discovery native and structured. This is atypical and signals deliberate agent-first positioning.
• ›Pricing was intentionally not disclosed in any fetched artifact or page. The agent correctly identified this gap and explained it as typical for German law practices, but could not verify the firm's actual fee structure, models (hourly vs. contingency), or competitive positioning on cost.
• ›Steps [6-11] (services, about, contact, FAQ, cases) all returned unreadable HTML with no text extraction, suggesting the site is a Next.js SPA. The agent could not access detailed case results, fee schedules, or service boundaries from these pages despite them existing.
• ›The agent had to rely on prior knowledge (RVG fee regulations, typical legal practice models) to explain the absence of pricing rather than discovering it from the site itself, weakening the grounding of that part of the answer.