A backward Kolmogorov equation lifts diffusion policies to a Cameron-Martin space, replacing stochastic score matching with a deterministic PDE. This approach achieves convergence bounds tied to kernel effective rank, improves trajectory regularity, and enables a deterministic failure detector without rewards. Validation shows 17% higher reward on PushT and 28.4% lower RMSE on a manufacturing line, with 96% reduction in deadlock events via Hamilton-Jacobi certification.