The agent successfully assembled a comprehensive explanation of DragonflyDB by combining fetches from the site's main pages (homepage, pricing, features, docs, what-is-dragonfly) with web searches that surfaced blog posts and comparisons. The site is heavily JavaScript-rendered, forcing the agent to rely on 55% of fetches from previous artifacts and 27% from prior knowledge; while key pages exist and were reachable, their actual content was not machine-readable in the static HTML responses, requiring the agent to bridge gaps through search results and external sources.

• ›The homepage, pricing page, and dedicated 'what-is-dragonfly' page all returned 200 responses but their HTML was truncated or JS-heavy, meaning the agent could not extract text-based content directly from these fetches—it had to rely on search results and prior knowledge to answer core questions about what Dragonfly does, who it's for, and how it's priced.
• ›Web searches (steps 11, 12, 18, 22, 23, 25, 26, 28) were critical: they surfaced blog posts ('How Dragonfly Delivers 80% Lower Costs Than Redis'), comparison pages ('Dragonfly vs Redis'), and docs URLs that the agent then cited in its final response. These searches, not the direct fetches, provided the substantive content.
• ›The agent correctly identified that a 'use-cases' page (step 8) and a 'vs-redis' page (step 9) do not exist (404s), showing it attempted a logical navigation pattern but the site structure doesn't support those URLs. Instead, it discovered the same comparison content via search.
• ›Pricing details (two tiers: open source free + cloud managed) were extracted from search results and docs links, not directly readable from the /pricing page fetch. The agent had to construct the pricing tier breakdown ($8/GB, $11/GB, backup costs) from multiple fragments across search results.
• ›The agent correctly surfaced confusion points about the site: JS-heavy rendering, unclear pricing tier specs, vague free trial terms, buried API compatibility limitations, and sparse company information—all of which reflect genuine gaps in how the site publishes structured information for agent consumption.
• ›The site lacks prominent structured data (schema.org, JSON-LD) or agent-friendly formats; comparisons, use cases, and licensing details are scattered across blog posts, docs, and FAQ rather than centralized on navigable landing pages.