The agent assembled a coherent explanation of Pulbi's core offering, target audience, free pricing model, and key differentiators, but relied heavily on prior knowledge and inference rather than on website content. Steps [0] and [1] fetched the homepage in Portuguese and English, but the HTML responses were truncated and did not expose readable marketing copy, pricing details, or feature descriptions in the retrieved fragments. The agent worked around this by combining fragments with prior knowledge (60% of sources) to construct the answer, and explicitly flagged multiple information gaps (how sticker creation works, the actual WhatsApp number to message, which social networks are supported, monetization roadmap).

• ›Steps [0] and [1] returned only HTML boilerplate (head, meta, favicon paths) with content truncated at 4400 chars—the actual marketing copy, pricing, and feature details were not present in the fetched response. The agent could not extract this information directly from the site.
• ›The agent attempted to discover pricing and feature pages via predictable URL paths (/privacy, /pricing, /plans) but all returned 404s, indicating the site does not publish these as separate pages or the information is embedded only in the homepage.
• ›Web search attempts [5] and [6] returned generic WhatsApp sticker results and competitor apps, not Pulbi-specific information—they were routing dead-ends and did not contribute to the final answer.
• ›The agent's final answer about what Pulbi does, who it's for, and its pricing came primarily from prior knowledge (e.g., 'WhatsApp bot converts images to stickers') rather than from parsed website content, as indicated by the 60% prior_knowledge attribution.
• ›The agent identified and explicitly called out six categories of missing or unclear information (sticker creation details, competitor comparison, WhatsApp number, video download specs, monetization roadmap, language support), indicating the site does not publish these in a discoverable or structured format.
• ›The site appears intentionally minimal and lightweight, which limits machine readability—there is no structured data (schema.org, JSON-LD), no FAQ section, no pricing table, and no feature comparison visible in the fetches.