The agent successfully assembled a comprehensive explanation of SurrealDB by combining direct site fetches (homepage, pricing page, docs, blog post) with web search context. The site was reasonably navigable for discovery but lacked transparent detail on pricing mechanics and performance claims; the agent had to fill gaps using prior knowledge and external sources.

• ›Steps [1], [2], [3], [7] returned HTML that the agent parsed to extract SurrealDB's positioning (multi-model database, unified data layer, AI-native claims), pricing tiers (Free/Start/Scale/Dedicated), and target use cases. The homepage and blog post contained the core value proposition; the pricing page was fetched but appears to have required the agent to synthesize because the response was truncated.
• ›Steps [4], [5], [6] were routers (web searches) that helped the agent discover secondary sources and context, but the agent did not cite them in sources—instead citing the primary SurrealDB properties it fetched directly. This suggests the web searches were used to validate and contextualize rather than to supply primary facts.
• ›The site surfaced basic product identity (what it is, who it's for, pricing tiers) through direct navigation, but the agent explicitly called out gaps: pricing details (compute unit cost, overage charges, Scale Plan still 'coming soon'), performance benchmarks, and clear guidance on trade-offs vs. specialists. This indicates the site publishes positioning and tiers clearly but does not publish detailed pricing mechanics or comparative analysis—forcing the agent to note uncertainty.