Agentic search is an exciting new frontier, but most solutions focus only on what happens after the query arrives: agentic orchestration of retrievers, rewriting queries, reranking results. We'll go further: the same agent reasoning that adapts queries at runtime can also reshape what gets indexed and how. Using Lucille, an open source ETL framework, as the ingestion backbone, we'll show how agents can augment documents, analyze content for retrieval-relevant signals, and align the OpenSearch index with the agentic tooling that will query it. On the query side, we'll cover query understanding, adaptive hybrid search, query rewriting, and reranking driven by live diagnostics: confidence gaps, score variance, agent reasoning. And we'll close the loop with LLM-as-judge signals that flow back into ingestion decisions. Demos will draw from real use cases where retrieval problems unsolvable through query tuning alone became tractable by re-modeling the data with agentic help.
Attendees will gain a blueprint for building search systems that think end-to-end, from smarter indexing to self-improving retrieval and relevance, and the open source tools to start building one today.