The agent assembled a comprehensive explanation of app.zama.org by working around the app's own inaccessibility—the domain returned a security checkpoint rather than a usable page. Instead, the agent pieced together understanding from Zama's main website, documentation, and third-party comparative articles. The task was satisfied: the agent correctly explained what Zama does (encrypted asset management via FHE), who it's for (privacy-conscious crypto users), pricing (minimal FHE-specific fees, free core use), and how it differs from alternatives (FHE computation vs. zero-knowledge proofs, layer-2 rather than separate chain). The site itself was poorly navigable for this task—the actual app was behind a security wall, forcing reliance on external sources and prior knowledge.

• ›Step [5] (zama.org homepage) and [6] (zama.org/about) provided positioning and context, but neither explained the app's user-facing features in detail—they focused on protocol-level benefits and company mission.
• ›Step [7] and [11] (docs.zama.org protocol documentation) returned technical infrastructure docs (FHEVM, protocol apps, SDK) but not end-user app documentation; the agent had to infer app capabilities from protocol docs.
• ›Step [12] and [15] (web search results linking to comparative articles and technical overviews) were actually more useful than official Zama pages—third-party sources like hinkal.io explicitly compared Zama to alternatives and explained the FHE vs. zero-knowledge distinction.
• ›The actual app.zama.org domain was completely inaccessible (step [1] returned Vercel security checkpoint), forcing 83% of successful fetches to come from web search rather than direct navigation.
• ›Pricing details were scattered and incomplete: the agent found fee structures mentioned in passing across multiple sources but no dedicated pricing page, calculator, or tier documentation on the official site.
• ›The site publishes technical documentation and high-level positioning but does not publish clear end-user-facing documentation on what the app does, how to use it, or what it costs—this forced the agent to assemble an answer from fragments, protocol docs, and external comparisons.