The agent successfully gathered enough information to explain Formasty's core positioning, target users, pricing tiers, and differentiation. It assembled a coherent answer from multiple content-bearing steps—primarily the landing page text file, pricing markdown, and the expanded LLM context document—all of which were either guessed or discovered via internal site references. The site was highly navigable for an AI agent, publishing machine-readable versions of its marketing content (llms-full.txt, pricing.md, landing-page.txt) that directly answered the task without requiring visual page parsing.

• ›Step [6] (landing-page.txt) and [3] (llms-full.txt) delivered the clearest product summary in plain text, eliminating the need to parse HTML. These were discovered via prior knowledge / internal site structure rather than navigation.
• ›Step [4] (pricing.md) provided pricing tiers and plan details in structured markdown format, making pricing information immediately machine-readable without scraping rendered HTML.
• ›Step [7] (alternatives.md) existed and was discovered but was not deeply leveraged in the final answer; the agent noted it but did not extract specific competitor comparisons, suggesting either truncation limits or that the content-bearing summary was complete without it.
• ›The site published explicit machine-readable artifacts (/llms-full.txt, /llms.txt, .md versions of pages) designed for LLM consumption, reducing friction and ambiguity in discovering what the product does.
• ›No web search was needed (0% sourced from search); the agent relied on prior knowledge and the previous artifact (88%), indicating the site's own documentation was sufficient and self-contained.