The agent successfully assembled a coherent understanding of Connor Love's freelance web development business by combining the homepage, About, Projects, Articles pages, and the AI-readable llms.txt summary. The site's pricing information is completely absent — there is no pricing page, rate sheet, or cost transparency, forcing the agent to note this as a critical gap and rely on inference (a 'BOOK A CALL' link) to explain how pricing works. The site itself is navigable and returns content, but is heavily interactive/JavaScript-rendered, making raw HTML retrieval less useful than the machine-readable llms.txt artifact.

• ›The llms.txt file ([6]) was the most agent-friendly resource on the site — it provided a concise, structured summary of who Connor Love is, what he does, and how to contact him. This artifact was pre-generated and immediately useful, whereas the HTML from pages [0, 3, 4, 5] required parsing and reassembly.
• ›The site lacks any pricing or service-tier information. Standard discovery routes (/pricing, /work) returned 404s ([1, 2]), and a direct web search for pricing on the domain ([8]) yielded no results. The agent had to infer that pricing is custom/consultation-based from the presence of a 'BOOK A CALL' link, not from explicit site content.
• ›The site is built with Next.js and includes interactive elements (Three.js, canvas animations), which means the raw HTML responses were mostly boilerplate and not content-bearing. The agent relied on prior knowledge (14% of fetches) and the llms.txt artifact (86% from previous_artifact) rather than parsing rendered page content, indicating the site is designed for human visual browsing, not machine extraction.
• ›The agent could identify what Connor Love does (custom web dev, React/Next.js, design systems) from combined fragments across pages, but could not answer 'how it's priced' with specificity — only that pricing is not public and requires a consultation call.