The agent successfully understood Statamic's core positioning, target audience, and pricing model by combining homepage exploration, dedicated feature/pricing pages, and a competitive comparison page. However, the site's heavy JavaScript rendering made detailed feature breakdowns inaccessible from raw HTML, forcing the agent to rely on web search results and external sources to fill gaps in pricing tiers and feature lockage—a significant friction point that required 'heavy bridge' workarounds despite the task being marked satisfied.

• ›Steps [1], [3], [4] returned metadata-rich but JavaScript-dependent pages; the agent extracted positioning from title tags ('answer to your frustrating CMS problems') and meta descriptions but could not parse rendered feature tables or detailed pricing breakdowns from the HTML alone.
• ›Step [2] (pricing page) returned 200 but was JavaScript-heavy; the agent could not extract actual price points ($349 Pro, $240/month Platform) from the fetched response and had to rely on web search results and prior knowledge to fill in specific costs.
• ›Step [9] (/vs/wordpress) was successfully cited and provided comparative framing, but again relied on JavaScript rendering for the actual comparison table, which the agent noted as unfetchable ('extracted details...I couldn't extract the details').
• ›Step [13] (Platform pricing page) was fetched but similarly constrained by client-side rendering; the agent used search results and external sources (G2, Capterra, IT Markerz) to construct the final pricing table rather than the site's native content.
• ›The agent made intelligent use of web search (steps [7], [8], [10], [11], [12]) to discover comparison pages and validate pricing, but this indicates the site does not surface enough machine-readable content on its own—a core identity/discovery problem for LLM-based evaluation.