Building reliable agentic AI systems

Martin Fowler discusses the challenges of building reliable agentic AI systems, highlighting the need for robust testing and evaluation methods to ensure AI systems behave as intended. This matters because AI systems are increasingly used in critical applications, and their reliability is crucial. To build reliable agentic AI systems, developers should focus on testing for edge cases, evaluating AI systems in realistic environments, and using techniques like model interpretability to understand AI decision-making processes. This requires a shift in approach, from traditional software development methods to more nuanced and adaptive approaches that account for the complexities of AI systems.