The agent successfully assembled a comprehensive explanation of Clera by combining direct site fetches (homepage, pricing, about pages) with web search results and third-party review sites. The primary getclera.com pages were heavily JavaScript-rendered and yielded minimal text in the raw HTML fetches, forcing the agent to rely on external sources (Stork.AI review, TechRSeries article, search snippets) to construct most of the substantive answer. The site itself is poorly machine-readable for initial discovery, but the agent compensated effectively through search-driven discovery.

• ›Steps [1], [3], [4] fetched getclera.com directly but returned truncated HTML with minimal semantic content (JavaScript-heavy rendering); the agent could not extract meaningful details from these pages alone.
• ›Step [7] search result snippets (especially the pricing link) provided the first concrete facts ('Free for Talent, 15% Success Fee for Companies') that appear directly in the final answer.
• ›Step [9] (Stork.AI review page) and step [13] (TechRSeries article) were the actual content sources that explained Clera's core model (AI talent agent, warm introductions, candidate-first positioning), not the official site itself.
• ›The agent explicitly cited external sources in its answer (Stork.AI, TechRSeries, Trustpilot, FunBlocks) rather than getclera.com pages, indicating the official site did not surface the required information in a readable form.
• ›Step [5] (404 on /features) shows the site's navigation surface is incomplete; the agent had to work around missing pages via search.
• ›The 'heavy_bridge' friction classification reflects that the agent needed external sources and web search (43% sourced from search, 43% from previous artifacts) to succeed, despite direct site access.