The Trust Gap: How to Actually Verify AI Agents

Most AI agents claim confidence without any verification mechanism, creating significant safety risks in production systems. A three-layer trust framework—combining output verification, confidence calibration, and performance history tracking—enables organizations to measure and validate agent reliability instead of relying on self-reported confidence. This shift toward measurable, contextual trust is essential for safe and scalable AI agent deployment in critical business operations.