Behavioral test coverage in AI applications allows teams to assess whether changes improve or degrade performance, similar to traditional software testing.

The challenge of ensuring AI systems do not exhibit undesired behaviors is more complex than simply achieving high performance metrics.

The behavior of AI systems is not just about what they produce but also how they produce it, which can significantly impact user trust.

Organizations must adapt their performance metrics to account for potential biases and undesired behaviors in AI systems over time.

Many organizations focus on high-level performance metrics, which can mask undesired behaviors in AI systems, leading to a lack of robustness.

Testing is essential for ensuring that AI systems behave as expected, not just in terms of performance but also in their underlying behaviors.

Key metrics for evaluating AI agents include deflection rate, which measures how many conversations the AI can handle without human escalation, and customer satisfaction scores, which reflect user experience.

Quality management is vital for AI agents, involving metrics analysis and deep feedback on user interactions to continuously improve performance and address any issues that arise during deployment.

Testing AI systems should involve both atomic behavior quantification and holistic regression testing to ensure reliability and performance consistency.