The agent successfully gathered comprehensive information about InVideo AI's purpose, pricing, target audience, and competitive positioning by relying almost entirely on third-party review sites and web search results, since the primary site (ai.invideo.io) is a JavaScript-heavy single-page application that does not expose pricing or feature details in static HTML. The agent provided a detailed, well-sourced answer that directly addresses all four aspects of the task, though it had to work around the site's poor machine-readability by pivoting to external sources.

• ›Steps [1], [3], [4] fetched ai.invideo.io directly but returned only HTML scaffolding and no content — the site is client-side rendered and does not serve pricing, features, or positioning data in a fetch-friendly form.
• ›Steps [6], [8], [11], [12] were web searches that surfaced third-party review aggregators (Capterra, Fluxnote, AI Stack Picks, Guideflow, Monday.com, Digen) as the actual content sources; steps [9] and [10] fetched two of those reviews, which contained the pricing tables, feature comparisons, and competitive analysis used in the final answer.
• ›The agent correctly identified the site's JavaScript-heavy architecture as a blocker ([5] notes this explicitly) and pivoted to search-sourced alternatives without hesitation — a sensible workaround, but it means the primary domain itself did not answer the task.
• ›All eight citations in the final response point to external review sites, not to ai.invideo.io or invideo.io — the agent had to assemble its answer from aggregated third-party data rather than first-party documentation.
• ›The agent flagged specific ambiguities it could not resolve even from the external sources (credit system opacity, watermark details, feature-tier boundaries), showing awareness of incompleteness — these gaps exist because even third-party reviews do not fully document InVideo's pricing model.