The agent assembled a partial understanding of Plearnly by combining the homepage title tag (revealing Korean language + TOPIK focus), sitemap discovery, and visits to feature pages (courses, membership, AI tutor, mentors, community, level-test, resources). However, pricing details remained inaccessible despite retrieving the membership page—the HTML was truncated and lacked rendered pricing information. The site proved navigable via sitemap but poorly suited to agent scraping: pages are JavaScript-rendered single-page app artifacts with pricing and detailed feature descriptions missing from the fetched HTML source.

• ›Step [1] title tag revealed core positioning ('เรียนภาษาเกาหลีออนไลน์และเตรียมสอบ TOPIK') but HTML was truncated, preventing extraction of hero copy or feature descriptions.
• ›Step [25] (sitemap.xml) was the critical discovery tool—it exposed feature page URLs the agent could not have guessed, and all eight subsequent feature pages (steps [27–37]) were reached via sitemap routing.
• ›Steps [27–28, 29, 32, 34, 35, 37] all returned truncated HTML (4400+ chars with '[truncated, XXXX more chars]'), indicating the agent's fetch layer cannot render JavaScript-heavy components where pricing, feature lists, and CTA copy likely live.
• ›Step [28] (membership page) explicitly promised pricing ('สมาชิกคอร์สภาษาเกาหลี | แพ็กเกจ 30 วันและรายเดือน') but the response was truncated, leaving the agent unable to extract actual price points or tier names.
• ›The agent correctly identified limitations in its own response (pricing, free vs. paid boundary, instructor qualifications) but these gaps were not recoverable via web fetch—they require JavaScript execution or manual site interaction.
• ›Search steps [8, 17–18, 31, 39] returned competitor references (Duolingo, LingoDeer, TOPIK prep alternatives) but yielded no Plearnly-specific pricing or review data, forcing the agent to infer differentiation from feature presence alone.