The agent successfully understood migueldevs.com by fetching the homepage, llms.txt, llms-full.txt, and projects page. The site is highly agent-ready: it explicitly publishes structured, machine-readable summaries (llms.txt and llms-full.txt) that directly answered what Miguel does, his experience, and his project portfolio. The agent assembled a complete picture of the site's purpose (professional portfolio), audience (employers, clients, collaborators), and positioning (rare expertise combination) with minimal friction.

• ›The site proactively publishes llms.txt and llms-full.txt files containing comprehensive, AI-readable summaries of Miguel's background, expertise, projects, and availability. These files were the primary content sources and directly answered most of the task's questions.
• ›The agent discovered these machine-readable resources via prior knowledge (direct URL guessing), not via site navigation or discovery—the homepage HTML truncated before revealing these links, yet the agent knew to fetch them anyway. This suggests strong convention adherence but also that a human visitor might not find them without explicit signposting.
• ›The site is transparent about technical skills, project scope, and outcomes (with quantifiable results like '$250K funding' and 'concurrent connections'), but deliberately omits call-to-action, contact info prominence, rates, and engagement model clarity—likely intentional portfolio design rather than navigation friction.
• ›All four content-bearing steps returned information directly cited in the final response's Sources section, indicating the agent built its answer entirely from fetched content rather than inference or prior knowledge assumptions.