AI agents are autonomous software systems designed to perceive their environments, make decisions, and act independently to achieve goals, offering a shift from traditional reactive software to proactive systems capable of handling complex, ambiguous situations. Their importance lies in automating decision-making processes, which necessitates rigorous testing and evaluation to ensure reliability and prevent unpredictable behaviors that may lead to production failures and compliance issues. Comprehensive testing methodologies, such as functional, safety, robustness, and integration testing, are essential to uncover failure modes and ensure agent reliability, while evaluation techniques should focus on metrics beyond accuracy to assess task performance, safety, and behavioral consistency. Effective testing and benchmarking involve using advanced tools and techniques, like simulation environments and model checking, to address challenges like non-deterministic behavior and emergent properties, ensuring that benchmarks remain relevant and predictive of real-world performance. As AI agents increasingly transform enterprise operations, developing robust internal capabilities for their evaluation and testing is critical to building stakeholder trust and avoiding the pitfalls of deploying untested autonomous systems.