The agent assembled a comprehensive overview of Zalify by combining direct site navigation with web search results and prior knowledge, successfully answering what Zalify does, who it's for, how it's priced, and how it differs from alternatives. The site was only partially navigable due to heavy JavaScript rendering that obscured page content; the agent worked around this by fetching multiple product pages (Z1, Reach, Ana), services, and pricing, then augmented with search results to fill gaps. Pricing details remained somewhat ambiguous because the actual tier breakdown wasn't fully visible in the fetched HTML.

• ›Steps [1], [3], [7], [8], [9], [13], [14] returned Next.js-heavy HTML that truncated meaningful content, forcing the agent to infer structure from metadata, URLs, and naming patterns (e.g., /z1, /reach, /ana for the three product lines).
• ›Step [3] (pricing page) was fetched successfully but the HTML response was truncated and heavily JavaScript-dependent, leaving the exact tier names and feature breakdown unclear—the agent had to reconcile conflicting tier naming (Free/Basic/Pro/Premium vs. Starter/Growth/Scale) from different sources.
• ›Web search steps [11], [12], [15], [16], [17], [18] provided the most actionable content: search result titles and metadata revealed product positioning, Shopify Premier Partner status, app store presence, and competitive positioning. These searches served as the primary source for differentiators and alternatives.
• ›The site's structure is guessable but not self-documenting: the agent correctly inferred product URLs (/z1, /reach, /ana, /services) using prior knowledge, and some guesses (like /products, /features) returned 404s, indicating limited site discoverability through standard navigation.
• ›The agent was unable to access actual page body content due to JavaScript rendering, so it cited URL sources rather than page content, relying heavily on search result titles and the Shopify app store as primary truth sources for features and customer feedback.