The agent successfully gathered enough information to explain Anam's core offering, target audience, and differentiation, though pricing information required assembly from external sources rather than direct site clarity. The homepage and pricing page were framework-rendered and truncated in fetches, forcing reliance on third-party explainers (selviaai.com, spatius.ai, aiapps.com) and web search to construct a complete picture. The site's own pages contain the information but are not readily machine-readable due to client-side rendering.

• ›Steps [1] and [3] returned Framer-rendered HTML that was truncated; the actual pricing and feature details were not in the fetched response bodies, only in meta descriptions ('Low-latency conversational video AI for customer service, training, and sales'). The agent had to infer and search for concrete details.
• ›Step [8] (selviaai.com/anam-pricing-explained) was the primary source for the pricing confusion discovery and effective cost calculation ($0.20/min vs. advertised $0.11–0.16/min). Step [9] (spatius.ai comparison) and step [12] (aiapps.com review) provided competitive positioning and use-case details. These third-party sources were more complete and structured than Anam's own site.
• ›The site publishes via Framer (steps [1], [3], [10]), which relies on client-side JavaScript rendering. Fetches return minimal content; full page content would require JavaScript execution. No structured data (JSON-LD, microdata) was apparent in fetched responses, making it hard for agents to extract pricing tiers, feature lists, or alternative comparisons without executing the page or searching externally.
• ›Web search (steps [6], [7], [11], [13]) was critical because the agent could not reliably extract the task-required information (pricing structure, target use cases, competitor differences) from the main site pages alone. 57% of fetches came from web search, indicating the site did not self-contain the answers in a machine-readable form.