The agent assembled a comprehensive explanation of Fiddler AI by visiting the homepage, pricing page, product pages (guardrails, evaluations, agentic observability), and about page. The site was well-structured and navigable, with clear product positioning and feature pages, though pricing details remained somewhat opaque and competitor comparisons were absent. The agent relied heavily on prior knowledge (56%) to fill gaps, particularly around what Centor Models are and how the platform compares to alternatives.

• ›Steps [1], [3], [6], [7], [8] directly returned feature-specific content (guardrails, evaluations, agentic observability) that populated the 'What Fiddler Does' section. These pages were semantically clear and linked from the homepage or reachable via educated guessing.
• ›Step [2] (pricing page) returned pricing tier information but lacked pricing calculators, minimum commitments, or concrete monthly cost examples—the agent noted this gap explicitly and flagged it as confusing despite the page existing and being findable.
• ›Step [9] (about page) provided company positioning but contained no customer case studies, testimonials, or deployment examples, limiting the agent's ability to infer 'who it's for' from social proof alone.
• ›The agent had to deploy significant prior knowledge (56% sourced from prior knowledge vs. 0% from web search) to contextualize features, explain Centor Models, and position Fiddler relative to competitors—none of which were explicitly covered in the fetched pages. This suggests the site does not self-document differentiators or competitive positioning.
• ›Failed 404s on /why-fiddler and /use-cases suggest the site structure lacked dedicated pages for common questions about differentiation and target use cases, forcing the agent to infer these from product pages.
• ›The site's information architecture prioritized feature pages (guardrails, evaluations, observability) over narrative explanation; the agent had to assemble a coherent story rather than find it pre-authored.