The agent successfully understood Kairos by assembling information from a combination of machine-readable text files (llms.txt and llms-full.txt) that the site explicitly publishes for AI consumption, plus Next.js-rendered pages. The site was navigable through structured artifacts, but pricing details and competitor comparisons remained unavailable in the agent's accessible fetches—requiring the agent to acknowledge these gaps rather than hallucinate them.

• ›Steps [23] and [25] were content-bearing and explicitly cited: the site publishes llms.txt and llms-full.txt as machine-readable briefs, which delivered the core value proposition, problem statement, use cases, and differentiators in plain text. This is excellent agent-readiness.
• ›Step [26] (kairos.rest/en/product) was cited in sources but returned boilerplate Next.js HTML with no accessible content. The agent extracted useful information from earlier steps instead, showing the site's HTML pages are less agent-friendly than its static text files.
• ›Pricing information was inaccessible: steps [3] and [28] (pricing page and pricing search) returned either boilerplate HTML or unrelated results. The agent correctly reported this gap rather than guessing, demonstrating integrity when source material is unavailable.
• ›No competitor comparisons were published on the site. The agent inferred competitive alternatives (MarketMan, WISK) from external industry context and prior knowledge (step [30] search), not from kairos.rest itself. The site takes a positioning approach of explaining what it does rather than what it beats.
• ›The agent relied heavily on prior knowledge (25%) and previous artifacts (67%), suggesting the site's main HTML is not self-contained; the llms.txt files are the reliable anchor for understanding what the product does.