Adaptive Relevance with Agentic Search
Session Abstract
Traditional search pipelines rely heavily on static query parsing and after-the-fact relevance analysis. In this session, we present a new paradigm: using LangGraph with OpenSearch to create an agentic-based system that can tune hybrid search in real time.
Session Description
We’ll review a multi-stage pipeline where live retrieval diagnostics—such as confidence gaps and score variance—dynamically adjust the blend of lexical and semantic search, trigger query rewrites, and rerank results. Then we’ll show how this measures up by benchmarking this adaptive approach against a static lexical baseline and a standard hybrid model. Attendees will leave with actionable frameworks for real-time query refinement of search systems.
Speaker Bio: Kevin Butler is a Search & Data Infrastructure Consultant at KMW Technology. He specializes in OpenSearch pipelines, adaptive hybrid search, and agentic LangGraph-driven workflows. His work focuses on bridging retrieval models with dynamic real-time decision-making.
Technical Level: Intermediate to Advanced (Familiarity with OpenSearch/Elasticsearch and basic agentic AI concepts assumed)
