This exploration delves into how AI agents translate conversational travel intent into precise venue selections. We examine the challenges of understanding nuanced traveller preferences, moving beyond keyword searches to interpret complex requests like "romantic anniversary dinner." This requires intelligent processing of mood, budget, and other contextual factors. We discuss how AI agents learn and apply individual preferences through AI Profiles and Character settings, enabling budget optimisation across multiple venues. The article also considers the necessary infrastructure, exploring whether sophisticated new systems are required or if existing discovery systems can be enhanced with improved query understanding. We analyse how AI interfaces should handle conversational intent, the structure of optimisation engines for budget and preferences, the necessary backend data structuring for intelligent matching, and how venues can best position themselves for intent-based discovery via Taash's Social Matching Engine and Context-Aware Discovery. The goal is to understand the infrastructure needed for AI agents to effectively translate "I want something nice" into concrete, optimal options for travellers.
