The agent successfully gathered comprehensive information about FindAffiliates by fetching the homepage, pricing page, markdown documentation files (pricing.md, submit.md, faq.md, tools.md), and the llms.txt artifact. The site exposed its core value proposition, pricing structure, and feature set in machine-readable markdown documents, making it straightforward for the agent to assemble a complete explanation. The agent delivered a well-organized breakdown of what the service does, who it serves, how it's priced, and how it differs from alternatives, though it had to note several gaps in the site's content (company background, competitive positioning, directory size, and vetting criteria).

• ›Steps [5], [7], [8], [9], [10] returned markdown documentation (.md files) containing detailed, structured information about pricing, program submission, FAQs, tools, and latest programs. This was the primary source of factual content.
• ›Step [6] (llms.txt) surfaced a curated index of all major pages and resources, acting as a discovery artifact that guided the agent to subsequent documentation pages. The site intentionally published machine-readable content.
• ›Step [1] (homepage) was fetched but returned HTML without readable text in the trajectory, forcing the agent to rely on subsequent markdown fetches rather than a conventional homepage narrative.
• ›The agent had to rely partly on prior knowledge to fill gaps: competitive positioning (ShareASale, CJ Affiliate, etc.) was not mentioned on the site; company background and vetting criteria were not documented; no case studies or testimonials were found.
• ›The site's agent-readiness is high for core information (pricing, features, tools) but low for differentiators and company context—the markdown docs answer 'what' and 'how much' but not 'why' relative to alternatives.