As generative AI systems become increasingly autonomous and adaptive, ensuring their trustworthiness poses new challenges. Traditional testing methods, such as static, scenario-based, and human-driven approaches, struggle to keep pace with models that continuously evolve and exhibit emergent behaviors. These systems face constant flux from model drift, tool drift, and shifting ground truths, creating a growing gap between what an AI system was verified to do and what it actually does in production.
Join Tariq King as he explores how generative AI itself can close this gap through the emergence of self-testing agents. Building on his early work in self-testing architectures and modern advances in agentic AI, King believes that the next frontier of trust will come from systems capable of autonomously generating, executing, and evaluating their own tests. By embedding continuous verification loops within their reasoning processes and applying risk-based principles to know when testing is sufficient, these agents offer a path toward trustworthy, adaptive, secure intelligence.
