The agent compiled a comprehensive explanation of Qianwen Open Platform by combining prior knowledge, a single homepage fetch, and three web searches—but could not access detailed pricing or technical documentation from the site itself. The homepage and docs pages returned only truncated HTML with no visible pricing tiers or feature details, forcing the agent to rely entirely on external sources and inference to answer the task. The site's dynamic React architecture and authentication gates made it largely opaque to static fetching.

• ›Step [1] (homepage fetch) returned only meta tags and truncated HTML—no substantive content about what the platform does, who it's for, pricing, or differentiation. All core task answers came from external web searches [6, 8, 9], not from the site itself.
• ›The pricing page [4] returned the same truncated boilerplate HTML as the homepage, with no actual pricing information visible. The agent had to note this explicitly as a gap and acknowledge that pricing details were not publicly fetchable.
• ›The agent's final answer was grounded almost entirely in web search results (Guru Focus, Baidu, news articles, pricing aggregators) and prior knowledge, not in content from open.qianwen.com. The site failed to surface its own business model, pricing, or technical documentation in a machine-readable way.
• ›The agent correctly identified that the site is a React SPA with dynamic content loading, which explains why static HTML fetches yielded no actionable information. This is a significant usability barrier for non-interactive agents.
• ›All three web searches [6, 8, 9] were explicitly cited in the Sources section, indicating the agent relied on them as authoritative. None of the site's own pages were cited, despite being fetched.