The agent successfully compiled a comprehensive explanation of MailDeck's service, positioning, pricing, and differentiation by fetching and synthesizing information from the homepage, pricing page, FAQ, alternatives comparison, about page, and markdown artifact versions. The site was highly navigable and agent-ready, publishing structured markdown versions (.md routes) alongside HTML pages, with a dedicated llms.txt file that clearly articulated the company's core value proposition upfront.

• ›Step [7] (llms.txt) provided a high-density summary of MailDeck's identity ('833K+ inboxes for 1,631+ clients', three infrastructure types, NOT a sequencer) that was explicitly designed for AI consumption and formed the backbone of the final explanation.
• ›Steps [8], [9], [10], [12] returned markdown versions of the same pages, providing structured, machine-readable content that answered pricing, FAQ, alternatives, and about sections without requiring the agent to parse HTML or reconstruct fragmentary data.
• ›The agent discovered these markdown resources by observing `<link rel="alternate" type="text/markdown">` hints in HTML (steps [2], [5], [11]) and by prior knowledge of the .md convention (steps [8], [9], [10] followed from previous steps' hints or direct pattern matching).
• ›Step [4] (alternatives page) explicitly compared MailDeck to named competitors, directly answering the 'how it's different' requirement without the agent needing to research external sources.
• ›The site surface its own positioning clearly: the FAQ (step [9]) and llms.txt (step [7]) both emphasize the sequencer vs. infrastructure distinction upfront, reducing the risk of misunderstanding.
• ›No web search was needed (0% search sourcing); 82% of fetches came from previous artifact links (markdown routes discovered from HTML hints), and 18% from prior knowledge (direct .md and /llms.txt guesses).