The agent successfully pieced together a comprehensive overview of Dr.Lably by combining the homepage fetch, pricing page, and multiple web search results. However, the site's navigation structure is weak—common pages like /features and /about return 404s, forcing the agent to rely heavily on web search results and prior knowledge (50% of fetches sourced from previous artifacts) to assemble a coherent answer. The final response is accurate and detailed but represents working around rather than through the site's native structure.

• ›Step [1] (homepage) and [2] (pricing page) returned valid 200 responses but their HTML was truncated and not extractable in the trajectory; the agent relied on web search to surface pricing details (₹1/report, ₹399/month, plan tiers) that should have been on the pricing page itself.
• ›Steps [3], [4], [7], [8] all returned 404s (/features, /about, /blog, /why-drlably), indicating the site lacks standard content architecture; the agent compensated by running 5 targeted web searches (steps [6], [11], [13], [14], [16]) to find on-site content at `/alternatives/dorays-alternative`, `/best-pathology-software-india`, and `/resources/lis-software-india-pricing-models-explained`.
• ›The agent cited 5 specific URLs in its final Sources section (steps [1], [2], [9], [10], and one more implied), but only steps [1], [2], [9], [10], [12], [13], [16] actually returned content; steps [9] and [10] (alternatives and comparison pages) were critical for positioning/differentiation claims but came from web search discovery, not site navigation.
• ›The agent explicitly acknowledged gaps in its answer ('couldn't extract full details,' 'not visible in search results,' 'scattered across the site') showing awareness that the site does not present its core value proposition (pricing plans, features by tier, differentiation) in a single, agent-readable location.
• ›50% of fetches were sourced from 'previous_artifact' (prior knowledge), indicating the agent had to supplement sparse or inaccessible on-site content with general knowledge about LIS software markets and pricing models to construct a credible narrative.