Beyond Top-K: Building search-confidence guardrails for agentic AI with OpenSearch

Agentic AI often fails because it treats top-ranked retrieval results as absolute truth, leading it to act confidently even when the information is irrelevant. To mitigate this, the author proposes implementing a "confidence layer" between retrieval and action that uses OpenSearch primitives to calculate composite confidence scores. This layer enables agents to recognize uncertainty and follow a graduated response, ranging from acting with caveats to complete abstention.