The agent partially satisfied the task by assembling an explanation of Zouroboros from the GitHub README and inferred context, but the official website itself was nearly non-functional as a source. The zouroboros.ai homepage and subpages returned React shells with minimal server-rendered content, forcing the agent to rely entirely on the GitHub repository README for factual material. The agent correctly identified major gaps: Zo Computer is unexplained, pricing for full-platform features is absent, and competitive positioning is unclear.

• ›Step [1] (homepage) returned only a React app shell—no substantive content about what Zouroboros does, who it's for, or pricing. The agent could extract only a title fragment ('An agent OS that rewrites its own wiring') and metadata.
• ›Step [9] (GitHub README) was the sole content-bearing source; it provided the core technical details: self-healing loop, memory architecture (episodic/procedural/cognitive), DAG execution, spec-first workflows, and dual installation paths (Zo Computer vs. standalone).
• ›Steps [3], [4], [5] (pricing, docs, about pages on zouroboros.ai) all returned identical React shells with truncated identical meta descriptions—indicating the site does not render or publish content server-side, making it unnavigable for agents without JavaScript execution.
• ›Web searches [10], [11], [12] were attempted but returned irrelevant results (Z.AI, Zorq AI, general framework comparisons) and did not help clarify Zouroboros specifically.
• ›The agent had to work around the non-functional website by pivoting to GitHub and inferring that Zo Computer is a separate platform dependency—a critical piece of context that the official site failed to communicate.
• ›The agent explicitly called out three major informational gaps that the site does not address: what Zo Computer is, whether the full platform has costs, and how Zouroboros compares to established competitors like LangChain or CrewAI.