The agent successfully gathered comprehensive information about Steel.dev's purpose, pricing, target audience, and differentiation, but had to assemble the answer from fragmented sources because the main pricing page redirected to the homepage. The site's documentation is well-structured and machine-readable (llms.txt, docs subdomain), but key commercial information (pricing) is not prominently surfaced on the marketing site itself, forcing reliance on docs and external search results.

• ›Step [1] (homepage) provided the core positioning ('browser infrastructure for AI agents') via meta description and title, but the pricing page ([2]) redirected back to the homepage rather than serving dedicated pricing content — a critical gap for understanding the commercial model.
• ›Step [11] (docs.steel.dev/llms.txt) was the richest single source, providing concise feature overview, SDKs, auth mechanisms, and API endpoints in plain text — well-structured for agent consumption and explicitly cited in sources.
• ›Step [7] (docs.steel.dev/overview/pricinglimits) returned the actual pricing details (free launch plan with $30 credits, $250/month scale plan, usage metering) and was found via web search, not surfaced from the homepage navigation, indicating pricing is buried in docs rather than prominently linked.
• ›Steps [3], [9] retrieved docs pages but responses were truncated HTML (4400+ char limit) — the agent inferred structure but did not extract granular content from these fetches; step [11] (llms.txt) worked around this limitation by providing machine-readable summary.
• ›The agent used web search twice ([5], [10]) to find pricing and alternatives information, indicating the marketing site alone did not expose these answers clearly; this partially fills the 'heavy_bridge' outcome (required external resources to close gaps).