The agent successfully gathered a comprehensive overview of Fat Voyage's core offering, methodology, and audience by fetching the homepage and machine-readable llms.txt file directly. The site was moderately navigable but left significant gaps around selection criteria, business model transparency, and scoring weights that the agent had to flag rather than fill from memory.

• ›The agent discovered both content sources by typing the domain from memory and following a link from the homepage to llms.txt — the site made both its main content and machine-readable index readily available, eliminating the need for search or multi-step navigation.
• ›Fat Voyage publishes llms.txt explicitly welcoming AI systems and providing structured, comprehensive metadata (hotel counts, scoring ranges, page inventory, contact channels) that directly answered questions about scope, methodology, and audience — this is agent-ready design.
• ›The agent correctly identified and reported a discrepancy between the homepage claim of '139 hotels' and llms.txt's reference to 'several hundred entries,' then triangulated the explanation (curated collection vs. full directory) from llms.txt's fine print rather than guessing, showing careful fact-checking.
• ›Multiple substantive gaps remain unresolved from the fetched content: hotel selection criteria, explicit business model and affiliate disclosure, precise scoring weights, and the definition of 'Fat traveller'—the agent did not attempt to visit the About & Methodology page directly, which might have clarified some of these.
• ›The agent could not compare Fat Voyage to alternatives because the site itself makes no such comparison; it inferred that Fat Voyage is 'editorial curation with published tiers rather than pure review aggregator' from model memory, correctly flagging this as inference rather than sourced fact.