The agent successfully assembled a comprehensive explanation of Rerun by synthesizing web search results and cited sources, since the landing page itself (a client-side rendered SPA) was not directly navigable. The agent found clear information about Rerun's purpose, target users, and differentiation from competitors, but had to work around missing pricing transparency and opaque SDK vs. Hub feature boundaries on the public website.

• ›The landing page and product pages are SvelteKit SPAs that do not render content in initial HTML, making them inaccessible to direct fetch. The agent relied entirely on web search results and external sources (blog posts, comparison articles, funding announcements) to answer the task.
• ›Search results [3, 4, 11, 12] returned blog posts, documentation snippets, and third-party comparison articles that contained the substantive information—what Rerun does, its components (SDK vs. Hub), use cases, and positioning. These were content-bearing because they directly addressed task requirements (purpose, audience, differentiation).
• ›Pricing was the weakest point: Steps [7, 9] aimed to fetch pricing pages but returned unparseable SPA shells. The agent disclosed this gap explicitly and noted pricing is 'usage-based' and 'not clearly displayed,' relying on secondary sources rather than primary documentation.
• ›The agent correctly identified that Rerun's positioning ('data layer for Physical AI') is abstract and requires interpretation; the distinction between free SDK and paid Hub is not clearly separated in marketing, creating ambiguity about what features are free vs. commercial.
• ›The site is poorly agent-ready: pricing, feature boundaries, and free tier limits are not published in machine-readable or even easily readable form on the primary domain. The agent had to assemble answers from external sources, blog posts, and comparisons rather than product pages.