OpenSearchCon Europe 2026

Search relevance tuning is one of the hardest—and most expensive—problems in modern search systems. It demands deep expertise in Lucene scoring, complex query DSLs, and iterative manual testing. While tools like OpenSearch User Behavior Insights (UBI) and the Search Relevance Workbench provide the data and the environment for improvement, the leap from "analyzing data" to "deploying a fix" remains a significant hurdle for many search engineers.

In this session, we introduce Agentic Relevance Tuning (ART)—an end-to-end framework that uses LLM-powered agents to automate the full relevance lifecycle in OpenSearch. ART replaces time consuming manual workflows with a coordinated system of specialized agents that continuously detect relevance issues, hypothesize improvements, and orchestrate offline and online evaluation.

By combining OpenSearch’s relevance tooling with the reasoning and orchestration capabilities of agents, ART transforms relevance tuning into a faster, more accurate, and more accessible process—enabling both beginner and expert search engineers to ship better search experiences to their customers.