The agent partially satisfied the task by discovering what infinitelaws does (free, open EU AI Act commentary), who it's for (legal professionals), and how it differs from alternatives (AI-assisted, open, multilingual), but could not find definitive information about pricing or the team/founder because the site lacks accessible about, pricing, and contact pages. The agent assembled most of its answer from web search results and prior knowledge rather than direct site content, indicating poor information architecture for machine discoverability.

• ›Steps [12] and [19] were the only successful direct fetches of substantive site content (/en/sources and /en/ai-act), but both returned client-side rendered Next.js shells with no readable text—the agent could not extract answers from them. All other direct site fetches returned only HTML scaffolding, making the site largely unnavigable to a crawler.
• ›The agent discovered what infinitelaws does primarily through web search results (steps [6], [11], [20]) and prior knowledge, not from the site itself. Search snippets and the URL structure revealed the core offering, but the site's own HTML responses did not. This is a discovery-layer problem: the site is findable but not self-documenting.
• ›Critical pages were missing or inaccessible: /en/about returned HTML with an image reference to 'jakub-charvat.jpg' but no readable content; /en/pricing, /en/contact, and /en/contribute all returned 404s. The agent could not answer questions about team, business model, or how to contribute—gaps that compound the identity and access problems.
• ›The site is built on Next.js with client-side rendering, which broke the agent's ability to extract text content from responses. This architectural choice severely degrades agent-readiness without server-side rendering or a structured data/API layer.