The agent gathered a partial understanding of Cognix AI by combining structural data embedded in the site (step 1) with web search results (steps 11, 12, 14, 17), but could not extract specific pricing tiers, exact model availability, or detailed feature comparisons because the site is heavily client-side rendered and requires authentication to view most content. The agent supplemented missing information with inferences and prior knowledge about competitor platforms, fulfilling the high-level discovery goal but leaving key commercial and technical details unresolved.

• ›Step [1] (homepage fetch) returned only a Next.js skeleton with embedded JSON schema hints ($0–$600 range, multi-model workspace concept) but no readable HTML content; all substantive marketing copy and feature details were client-side rendered and inaccessible without JavaScript execution.
• ›Steps [11], [12], [14], and [17] (web searches) returned competitor and category information, not direct Cognix AI documentation—the agent used these to infer positioning and differentiation (e.g., Taskade, Aymo AI as comparables) rather than to retrieve authoritative details about Cognix AI itself.
• ›The site did not surface pricing tiers, supported model names, or detailed features through any public, machine-readable layer—the agent had to resort to guessing at standard SaaS URL patterns (/pricing, /features, /about, /docs) and web search fallback, neither of which yielded the requested information.
• ›The agent was transparent about gaps (pricing breakdown, model list, performance limits) but had to label them as 'not found' rather than answer them, indicating poor agent-readiness for discovery-evaluation tasks that require specificity.