The agent successfully assembled a comprehensive overview of Reaktor by combining direct site fetches with web search results, but encountered significant gaps in pricing and engagement model details. The site's JavaScript-heavy design and lack of transparent pricing information meant the agent had to rely heavily on prior knowledge, search results, and inference to construct a complete answer—particularly for the 'how it's priced' component, which remains largely unanswered despite the task requirement.

• ›Steps [4], [5], [8], [9], [14] returned content about services, strategy, work portfolio, and about pages, but the responses were truncated HTML with heavy JavaScript dependencies that prevented full content extraction. The agent was citation-dependent on what it could infer rather than explicit text.
• ›Steps [11], [12], [16] (web searches) provided the most actionable content—search snippets surfaced competitor lists (G2), cultural differentiators ('no hierarchy'), and company structure info (employee count, IPO details) that were NOT visible in the site fetches themselves. This indicates the site does not publish key positioning data in readily accessible text form.
• ›The agent explicitly noted pricing as 'not transparently displayed' and had to infer the engagement model is 'custom, project-based quoting' from context clues rather than explicit pricing pages. No pricing URL exists in the cited sources, confirming the site does not surface this information machine-readably.
• ›Steps [5] and [14] were redundant (both About pages), suggesting the agent was navigating a site architecture it could not fully map due to JavaScript rendering constraints. The 'light_bridge' friction outcome aligns with this—the site required external search augmentation to fill gaps.