The agent was unable to load the target Vercel subdomain (tammanidelicafe-tammaniweb.vercel.app) due to compressed content delivery, but worked around this by discovering and fetching the underlying business's official website (tammani.cafe). The agent successfully assembled a coherent explanation of what Tammani does, who it serves, and how it differs from alternatives, but encountered significant gaps in pricing details and clarity about the purpose of the Vercel subdomain itself. The site's structure—split across a Vercel deployment and a separate main domain—created friction that required external search and inference to resolve.

• ›Step [9] (tammani.cafe/en) was the primary content-bearing fetch and is explicitly cited in the Sources section; it returned HTML from a Next.js site but the agent could not extract complete pricing or menu details from the rendered markup.
• ›Step [7] (web search for 'Tammani Deli Cafe') surfaced the official website URL and social media references, acting as the discovery bridge when the target Vercel subdomain failed to load meaningfully; the search results themselves were not content-bearing but enabled the successful pivot.
• ›The Vercel subdomain (step [1] and [3]) returned truncated HTML with no readable content extraction, leaving the agent unable to determine whether it was a portfolio, ordering system, or mirror of the main site—a key unanswered question about the task domain itself.
• ›The site does not publish structured pricing or detailed menu data in machine-readable form; the agent had to infer pricing from the mention of Uber Eats availability and lunch specials without concrete numbers.
• ›The agent had to rely on social proof signals (Instagram, Facebook, Uber Eats presence) and prior knowledge to fill gaps, indicating the site is not fully self-contained or transparent for agent readability.