Agentic AI: System-First Approach for Engineering Success

Agentic AI-generated outputs, even when appearing correct, can still fail in engineering systems. Here's the thing: while individual components like a model or code might look valid, true engineering correctness depends on the entire system's behavior. For example, an AI agent might modify a controller, and it passes local tests, but it could still cause unexpected behavior when integrated into the broader system. What's interesting is that these failures often emerge when different software, controls, and connected components interact. The bottom line is that engineering systems need a system-first approach to evaluate changes based on overall system behavior and requirements, not just individual outputs. This approach helps teams catch problems earlier and ensures agentic AI changes are consistent with the intended system.