The agent successfully assembled a comprehensive explanation of CoBuy by combining truncated website fetches with web search results, yielding a coherent understanding of what CoBuy does, who it serves, pricing, and differentiation. The site itself did not load complete content in fetches (all pages truncated), forcing the agent to rely on search results and prior knowledge to fill gaps; however, the agent was able to construct an accurate and detailed answer despite this friction.

• ›Steps [1], [3], [4], [12] (direct website fetches) all returned truncated HTML responses, making them unreliable for content extraction—the agent received only boilerplate/metadata, not the actual page copy. These fetches were cited as sources but did not materially contribute factual content to the answer.
• ›Steps [10], [15], [17], [19] (web search queries) returned link titles and snippets that exposed the platform's positioning (Co-ownerOS™, pricing tiers, governance workflows, founding team), allowing the agent to build the narrative. Search results were the primary content-bearing layer, not the site itself.
• ›The agent flagged a genuine pricing ambiguity (both $500/year and $1,000/year mentioned) and acknowledged it could not fully verify the feature set or user base due to page load truncation—appropriate intellectual honesty about incomplete coverage.
• ›The site is built with Next.js and relies on client-side rendering, which explains why server-side fetches returned incomplete HTML without JavaScript execution. This makes the site effectively opaque to crawlers seeking full content.
• ›The agent's final answer is well-structured and explains the value proposition (shared consensus-building vs. attorney/DIY trade-offs) using language that suggests it drew from search snippets and reasonable inference, not from complete on-site documentation.