The agent successfully compiled a comprehensive explanation of HeyGen's purpose, target audiences, pricing model, and competitive differentiation by combining direct site fetches (homepage, pricing, features, about pages) with external search results and help documentation. The site's JavaScript-heavy architecture rendered pricing and features pages inaccessible in raw HTML form, forcing the agent to rely on web search results and the help center to answer the core task questions. Despite this friction, the agent delivered a complete, well-sourced answer covering all four requested dimensions (what, who, how priced, how different).

• ›The homepage [1], pricing page [2], features page [6], and about page [4] were fetched but returned only HTML scaffolding with minimal readable content due to JavaScript rendering—the agent could not extract pricing tiers, feature lists, or positioning directly from these critical pages.
• ›The agent relied heavily on web search [14, 16, 18] and the help center article [13] (discovered via search) to answer all substantive questions about pricing structure, credit consumption, avatar capabilities, and use cases. These external sources, not the marketing site itself, provided the actual details.
• ›The agent explicitly called out confusions and unknowns (credit consumption clarity, avatar count variations, API pricing opacity, custom avatar costs) that it could not resolve from the site, demonstrating awareness of gaps between marketing messaging and operational details.
• ›HeyGen's own help center and third-party review sites (AutoGPT, eesel, Arcade, BIGVU) were far more machine-readable and agent-friendly than the marketing site's JavaScript-dependent pages.
• ›The agent's 50% reliance on prior knowledge (vs. 13% web search and 38% previous artifact) suggests it constructed answers partly from general domain knowledge about AI video platforms, not purely from HeyGen's published materials.