The agent successfully pieced together a comprehensive understanding of Edzy by combining content from the website's homepage, about page, FAQs, and comparison page with supplementary web search results. The site's heavy JavaScript-based architecture and missing dedicated pricing page required the agent to work around significant friction—key information like pricing and feature details were either dynamically rendered or absent from direct navigation paths, forcing reliance on search results and external app store data to complete the picture.

• ›Step [1] (homepage) and Step [10] (AI tutor guide page) provided foundational positioning and feature descriptions, but the HTML was JavaScript-heavy and the actual UI-driven content wasn't fully extractable from raw markup.
• ›Step [3] (about page) yielded company story and mission but lacked pricing/tier details; the agent had to search externally because Step [2] (/pricing) returned a 404—the site does not publish pricing on its own domain in a directly navigable form.
• ›Steps [6] (FAQs), [9] (vs. ChatGPT comparison), and [10] (AI tutor page) were explicitly cited in the final response's source list, indicating they were treated as authoritative content-bearing resources, even though the raw responses shown are truncated and don't expose full detail.
• ›The agent supplemented website content with web search (steps [5], [7], [8], [11], [12], [13]) to fill gaps—pricing came from external reviews and app store data, not from edzy.ai itself; this was necessary because the site does not expose a machine-readable pricing or feature comparison resource.
• ›The site's navigation structure is incomplete for this task: /pricing and /features both returned 404s, suggesting either intentional design (pricing only on app stores) or poor URL structure, making discovery-oriented tasks harder than they should be.