New Paper Proposes Framework for Measuring AI Agent Reliability

A new paper introduces a framework to quantify the gap between AI agent capabilities and their reliability in real-world tasks. The authors argue that current benchmarks overstate performance by ignoring failure modes like hallucinations and task drift. They propose standardized stress tests to evaluate agents under adversarial conditions, aiming to make reliability a measurable science.