The agent successfully understood Hobbyka by retrieving a single content-bearing resource—the llms.txt file—which contained a complete self-description of the company, its target audiences, business model, and differentiation. The site's homepage HTML was truncated and inaccessible, forcing the agent to rely on a machine-readable artifact file and prior knowledge to assemble the final answer; Hobbyka made discovery easy for AI agents but not for traditional web browsing.

• ›Step [5] (llms.txt) returned the only substantive content used to answer the task—it included company positioning ('producer and supplier of small architectural forms'), target audiences (architects, designers, procurement), pricing structure (invoice-based, variable delivery), and key differentiators (AI-first design, REST API, MCP server support).
• ›The agent guessed the llms.txt URL based on a meta tag in the homepage HTML (step [3]), not by following a navigation link or search result. This reveals the site is explicitly designed for AI discovery via well-known artifact paths rather than traditional information architecture.
• ›The homepage HTML (steps [1] and [3]) was truncated in both fetch attempts and contained no visible product catalog, pricing table, or company background—critical information for a human evaluator. The agent worked around this by relying on the llms.txt file and prior knowledge, not by navigating the site's UI.
• ›Step [7] (delivery page) returned only HTML headers with no content, suggesting the agent could not extract pricing or delivery details from the live website—all substantive delivery/pricing information in the final answer came from the llms.txt artifact, not from a delivery page.
• ›The site is highly agent-ready (publishes machine-readable self-description, provides API/CLI, supports MCP) but poorly navigable for humans in this trajectory—no product listings, categories, or pricing were accessible through traditional web fetches.