In this work, we compare a traditional design-ofexperiments (DoE) workflow, defined up-front and then run, against an agentic AI workflow that revises its plan as the data arrives. The use case is a chassis optimization on the standard compact example in the Modelon VehicleDynamics Library. Both workflows produce a recommendation. The DoE arrives at a clean Pareto front and a stiffness change. The agentic path is longer: along the way it flags a controller saturation in the DoE winner, traces a center-ofgravity asymmetry to a load sensitivity the original scope did not cover, and finds a kinematic ceiling outside the four DoE parameters. It ends up changing a different parameter, and that parameter turns out to do more than the static KPIs asked for: dynamic verification shows the bushing change roughly halves the fishhook return lag unloaded. The architecture that makes this possible combines validated physics, Modelon Impact, the Model Context Protocol that connects them to an LLM, and a knowledge base layered by lifetime. We close with what we learned about when each approach is the right tool.