OpenSearchCon India 2026

Large Language Model agents frequently rely on retrieval systems to make decisions, yet most pipelines still depend on a naive top-k retrieval strategy. In agentic workflows this can lead to a dangerous failure mode: agents confidently acting on low-relevance results, amplifying hallucinations through iterative tool use.

This session presents a practical framework for implementing search-confidence guardrails using OpenSearch. Instead of treating retrieval scores as opaque signals, we demonstrate how to convert heterogeneous ranking outputs into a normalized trust metric that agents can reason about.

We will explore techniques such as score normalization, Reciprocal Rank Fusion (RRF), and hybrid retrieval (BM25 + vector search) to build a deterministic confidence layer on top of OpenSearch queries. Using this signal, agents can dynamically decide whether to answer, re-query, or request clarification, preventing cascading hallucinations.

The talk includes a reference architecture for agent-search interaction, evaluation workflows using the OpenSearch comparison tooling.