The agent could not access decolar.com directly due to bot protection, so it assembled an understanding of the company entirely from web search results and prior knowledge. The final response covers what Decolar does, its target audience, pricing model, and competitive differentiation with reasonable completeness, but lacks verification of current features, exact fee structures, and post-acquisition changes. The site itself is not agent-navigable; the agent succeeded by working around the access barrier through search and inference.

• ›Search steps [3], [4], [5], [7], and [10] returned snippets and link titles that the agent cited directly (Crunchbase, ZoomInfo, Tracxn, Similarweb, ARQ Finance), but the agent could not fetch the full pages behind those links—it inferred from search metadata and prior knowledge of OTA business models.
• ›Decolar.com homepage returned a 403 with bot-detection CAPTCHA, making the primary source completely inaccessible. Crunchbase and Trustpilot also returned 403s, forcing reliance on search engine snippets and the agent's pre-trained knowledge of travel booking platforms.
• ›The agent filled significant gaps (fee percentages, exact loyalty program mechanics, post-May 2025 acquisition details) by drawing on general knowledge of OTA commission models and regional travel booking patterns rather than discovering them on the site. This is a workaround strategy, not native site discoverability.
• ›Only link preview text and search result titles were available; no deeply detailed pages about pricing, features, or competitive positioning were reachable. The agent explicitly acknowledged in its response that it 'worked entirely from third-party sources' and could not 'verify claims directly from their interface.'
• ›The site is not structured for agent discovery—it actively blocks bot access and does not expose machine-readable metadata, APIs, or structured data that would let an agent answer this evaluation task directly.