Engineering teams often turn expert workflows into custom apps. That makes sense because a few engineers may own the analysis code, simulation model, or test workflow, while many more users need to run it, inspect results, compare cases, and make decisions from the outputs. As these apps become broader team tools, the scale and breadth of the interaction start to matter. Users are working through more test and simulation data, and more of the value comes from the follow-up questions after the first result appears. That raises an important question about agentic AI and engineering apps: when does it make sense to add an agentic front end to a grounded engineering workflow—one where the analysis, models, and functions already exist and are controlled by the workflow owner?