The agent successfully assembled a comprehensive explanation of Fly.io's purpose, target audience, pricing model, and competitive positioning by combining direct site fetches (homepage, pricing, docs, about) with heavy reliance on web search results and prior knowledge. The site's marketing-forward homepage and fragmented information architecture forced the agent to conduct 6 follow-up web searches to piece together coherent answers about what Fly.io actually does, who uses it, and how it's priced—indicating moderate navigability for agents seeking structured product understanding.

• ›The homepage (step [1]) fetched successfully but provided only marketing language ('Computers for agents', 'Sandboxes aren't enough') without clearly explaining what Fly.io is. The agent had to search externally to clarify that it's a PaaS for global app deployment.
• ›The pricing page (step [2]) returned raw HTML but was insufficient alone—the agent needed 4 additional web searches (steps [11], [8], [9], [10]) to construct a complete pricing breakdown across compute, storage, and databases, and to contextualize pay-as-you-go model changes (October 2024 switch from subscription tiers).
• ›Critical information was scattered across the site: docs at /docs (step [3]), about page at /about (step [4]), but no dedicated features or comparison page (/features returned 404 at step [5]). The agent relied on external sources and prior knowledge (80% of fetches) to answer 'how it's different from alternatives'—Fly.io's site does not self-serve competitive positioning.
• ›Web search results (steps [7]-[12]) were more content-bearing than site fetches: comparison articles from DigitalOcean, Sealos, and technical blogs contained structured pricing tables, use-case summaries, and customer examples that the site's own pages did not explicitly surface in machine-readable form.
• ›The agent correctly identified confusing elements: the homepage tagline does not clearly communicate what Fly.io does for the majority of use cases (only recent AI agent focus), pricing is complex to predict on pay-as-you-go model, and documentation is fragmented across marketing site, /docs portal, and community forum.