Enterprise AI teams are granting agents more autonomy even as confidence in automated testing declines. A significant portion of enterprises report AI agents failing in customer-facing roles despite passing internal evaluations. Many organizations permit production deployments without human review or plan to do so soon. This creates an "evaluation gap" where agent autonomy outpaces assurance. Traditional testing methods are insufficient for agents with dynamic decision-making capabilities. Enterprises distrust automated evaluations due to poor alignment with real-world outcomes, bias, and lack of explainability. The core issue is that capability does not equate to consistency or reliability. Repeatability, therefore, must be a primary metric, with production incidents feeding back into testing. Autonomy should expand based on demonstrated reliability and the consequences of failure. Low-risk actions can tolerate broader autonomy, while high-risk actions require stricter thresholds and human escalation paths. The market will continue to favor greater autonomy, but success hinges on prioritizing repeatability and regression testing over deployment speed.