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Explore how to build AI-native search infrastructure that transforms web data into structured, real-time intelligence for autonomous agents in this 18-minute conference talk. Learn why traditional search systems designed for human interaction fall short for AI agents that require machine-readable context and structured data. Discover the architecture behind crawling and enrichment pipelines that retrieve, normalize, and integrate web intelligence directly into large language model applications. Examine entity and event modeling techniques alongside hybrid vector-lexical retrieval systems that enable both broad exploration and precise data lookups. See practical demonstrations of how fresh, structured web context empowers AI agents to perform complex tasks like market monitoring, competitive analysis, and research synthesis with traceable citations. Understand the critical design decisions, latency considerations, and reliability safeguards necessary for production-grade systems. Gain insights into planning algorithms, tool integration strategies, and citation mechanisms that enable agents to work with real-time web data. Walk away with a comprehensive understanding of how reimagining web infrastructure for AI agents rather than human users opens new possibilities for autonomous systems that can intelligently interact with and synthesize information from the dynamic web environment.
