The text argues that while accuracy metrics are often used as the primary indicator of AI agents' reliability, they fail to capture the complexities and challenges faced in real-world production environments. Vikram Chatterji, CEO of Galileo, emphasizes that accuracy alone cannot account for the performance of AI systems under varying conditions, unexpected inputs, and edge cases. The article explores the importance of AI agent reliability metrics, which assess agents' behavior in real-world scenarios to ensure dependable performance. It highlights several key metrics beyond accuracy, such as consistency, robustness, uncertainty quantification, temporal stability, context retention, response latency, graceful degradation under load, and behavioral consistency across demographics. These metrics aim to provide comprehensive insights into AI agents' reliability, helping teams identify and address potential issues before they affect user experience and business outcomes. The article stresses the need for advanced measurement and monitoring methods to build truly dependable AI systems, advocating for a shift from traditional accuracy-focused evaluations to a broader reliability assessment.