The agent successfully gathered and synthesized the core information needed to explain Propane to someone else—what it does, who it's for, pricing model, and differentiation—by fetching the llms.txt, pricing.md, platform.md, and homepage markdown. The site was reasonably navigable via direct URLs and markdown artifacts, but the marketing copy deliberately trades specificity for simplicity, leaving gaps around usage-tier pricing and feature granularity that the agent honestly flagged.

• ›Step [2] (llms.txt) delivered the most complete product summary in machine-readable form—positioning, data source count, integrations, and compliance claims. This was a direct guess, not surfaced by the homepage, but it worked and provided the clearest answer about what Propane does.
• ›Step [5] (pricing.md) provided the flat-fee model (€50/month, unlimited users) and usage-based context pricing, but deliberately omitted specific per-context or per-competitor pricing tiers. The agent correctly identified this as a gap and called it out rather than inventing details.
• ›Steps [6] and [8] (platform.md and homepage markdown) reinforced the product positioning and use cases (product teams, AI agents, unified context) but added little new concrete information beyond what llms.txt already stated. The site's marketing is intentionally high-level.
• ›The site publishes markdown versions (.md extensions) of key pages, which made it agent-friendly; however, the agent had to guess the existence of llms.txt and .md artifacts rather than being directed to them by the homepage HTML. Discoverability of these resources relied on prior knowledge (83%) rather than site navigation.
• ›The agent noted several intentional omissions: vague 'context' definition, no feature inventory, no explicit competitor positioning, and underspecified AI agent workflows. These are marketing-strategy choices, not navigation failures, but they limited what the agent could extract.