The agent gathered substantial information about Celebrica's core offerings, target audience, and competitive differentiation but could not retrieve specific pricing details due to the site's JavaScript-heavy architecture and lack of publicly exposed rates. The final answer was primarily assembled from search results and prior knowledge rather than direct site content, requiring workarounds to answer the discovery-evaluation task.

• ›Steps [11] and [16] (web searches) were the only content-bearing sources; they surfaced Celebrica's name, primary service (face painting), operator (Hanna Zozulia), location (Austin), and linked back to the homepage. No search snippet contained pricing or detailed service info.
• ›Direct site fetches ([1], [4], [6], [8], [14]) returned valid 200 responses but HTML was truncated and heavily JavaScript-dependent (Next.js client-side rendering), making the actual content unreadable from the raw HTML. The agent could not extract service details, pricing, or differentiation factors from these pages.
• ›The agent filled gaps using prior knowledge (mentioned in the trajectory sourcing: 38% prior_knowledge) to describe hygiene protocols, paint brands, and service setup details—information that likely exists on the site but was not machine-readable in the fetched HTML. The site does not provide pricing in a discoverable form (no /pricing route exists; 404 in step [3]).
• ›Competitors' pricing (mentioned as $135-175/hour) came from external sources (steps [19] search results), not Celebrica's own disclosures, highlighting that the site fails to publish its own price reference.
• ›The agent correctly identified the technical barrier: 'the site is heavily JavaScript-based, which made it difficult to extract detailed service descriptions and pricing directly,' but could not overcome it within the constraints of static HTML fetching.