The agent partially satisfied the task by assembling a coherent explanation of LegionCode from the GitHub README and competitive landscape research, but encountered significant gaps in the site's own documentation. The website itself was minimally navigable for this discovery task—it lacks a pricing page (404), a detailed /cloud page, and feature documentation; the agent had to rely on the open-source repository README and external web search to construct the answer.

• ›Step [15] (GitHub README) was the primary content-bearing source and provided the core product definition ('open-source AI coding-agent workspace') and basic feature list. The site's own pages ([1], [4], [5]) returned HTML but no extractable content snippets were cited or used in the final response, indicating they were either sparse or not machine-readable.
• ›Steps [20] and [16] were web searches that provided competitive context (Claude Code, Vercel v0, Cursor) used to frame LegionCode's differentiation. The agent explicitly cited these sources, treating search results as content-bearing because they contained the comparison framework.
• ›The site actively hides critical information: /pricing returns 404, /cloud exists but is sparse, and pricing/timeline/model-provider details are entirely absent from legioncode.dev. The agent had to explicitly call out 'pricing is completely unclear' and 'Cloud offering is sparse' as unresolved gaps, indicating the site failed to surface answers the user asked for.
• ›The agent relied heavily on prior knowledge and external research (86% previous artifact + 14% prior knowledge per metadata; 0% web search credited, though searches were performed). This suggests legioncode.dev itself does not publish the information in a fetchable, discoverable form.