The agent successfully understood influence.bg's core business model, target audiences, pricing, and differentiation by extracting information from the site's llms.txt file and four content pages. The site exposed its value proposition via a well-structured machine-readable artifact (llms.txt), but otherwise relies on JavaScript-rendered pages that returned only HTML skeletons; the agent had to bridge this gap using the llms.txt data and prior knowledge of typical marketplace features.

• ›Step [6] (llms.txt) was the primary content source: it directly stated the business model ('Bulgarian marketplace for brands and creators'), payment structure ('90% payout to creators, 10% platform fee'), and key differentiators ('escrow via Stripe'). This artifact was highly agent-readable and delivered structured, citation-ready facts.
• ›Steps [9], [10], [12], [13] (for-brands, for-creators, help, for-agencies pages) returned 200 status codes indicating valid pages, but the HTML responses were truncated and JavaScript-heavy, revealing only boilerplate metadata and script tags—no rendered content. The agent could not extract additional detail from these pages beyond their URLs existing.
• ›The site is partially agent-ready: it publishes a comprehensive llms.txt that answers most of the task (what, who, how priced, differentiation), but lacks an English version or dedicated pricing/about pages (both returned 404). The agent had to assemble the full answer using one high-quality artifact rather than multiple independent pages, creating a single point of failure for discovery.
• ›Language-only barrier (Bulgarian) and lack of visible tiered pricing or feature comparison pages meant the agent could not fully explain 'what makes it different from alternatives'—it inferred positioning from the llms.txt's emphasis on 'escrow' and 'no subscription' rather than finding explicit competitive comparisons on the site.