The agent successfully assembled a comprehensive overview of Meta AI by working around the blocked official website (meta.ai returned 403) and instead synthesizing information from multiple third-party guides and pricing tools. The agent gathered details on what Meta AI does, who it's for, pricing tiers, and competitive positioning, though it had to rely entirely on web search results and external documentation rather than official sources, and called out several areas where information was ambiguous or hard to verify.

• ›The official meta.ai domain was inaccessible (403 Cloudflare challenge), forcing the agent to rely on web search results and third-party aggregators (AISO Tools, DataNorth AI, BuildFastWithAI, Artificial Analysis) as the sole source for answers about Meta's own product.
• ›Pricing information came from news articles (CNBC dated May 2026) and third-party pricing aggregators, not from Meta's own website—creating risk of staleness and inconsistency across sources that the agent correctly flagged as 'ambiguity'.
• ›The agent had to infer product positioning and feature comparisons from comparative guides rather than from Meta's own positioning statement, and explicitly noted confusion around model versions (Llama 3 vs 4 Scout vs Maverick), 'deeper thinking mode' specifications, and feature parity between web app and in-app integrations.
• ›17% of the agent's knowledge came from prior training data (general knowledge about Meta AI, Llama models, competitive landscape), while 83% came from web search—indicating the site publishes almost nothing in a directly-fetchable, official format.
• ›The agent successfully answered all four questions asked (what it does, who it's for, pricing, how it differs), but explicitly acknowledged that several details were confusing, unverified, or contradictory across sources—a honest articulation of partial confidence in the final answer.