The agent successfully gathered enough information to explain wattsave.kr's purpose, audience, and business model, but had to work around severely truncated HTML responses from the main pages. The RSS feed (step 15) and sitemap (step 17) provided the clearest machine-readable summaries; the contact and calculator pages (steps 19–20) confirmed the site's structure, but their content remained inaccessible due to JavaScript rendering. The agent relied heavily on prior knowledge of the site and inferred content from metadata rather than actual page text.

• ›Step 15 (RSS feed) was the most valuable content source: it contained the site's official description ('가전제품 전기요금을 공식 자료로 검증해 정리하는 사이트...'), mission, and founder context, all in plain XML. This single step anchored the entire explanation.
• ›Step 17 (sitemap.xml) revealed site structure and page hierarchy (calculators, articles, about, contact) in machine-readable form, allowing the agent to infer what services exist without being able to read the pages themselves.
• ›Steps 19–20 (contact and electricity-calculator pages) returned 200 OK but with truncated HTML payloads showing only head metadata. The actual page content—what makes these pages valuable—was not retrievable via the agent's fetch mechanism, likely due to client-side rendering requirements.
• ›The agent did not rely on web search results (step 5 returned generic results about electricity rates in Korea, not about wattsave.kr specifically) and instead pivoted to structural discovery (RSS, sitemap, guessing page URLs).
• ›Prior knowledge contributed 58% of the fetches, suggesting the agent had cached or pre-trained knowledge of the site's purpose and structure, which filled gaps left by inaccessible page content.
• ›The site does not publish pricing, competitive analysis, or detailed 'about' content in machine-readable or easily fetchable formats; the agent had to assemble the story from RSS metadata, founder backstory, and URL structure inference.