We study the structure of sample paths of Markov systems by using performance potentials as the fundamental units. With a sample path-based approach, we show that performance sensitivity formulas (performance gradients and performance differences) of Markov systems can be constructed intuitively, by first principles, with performance potentials (or equivalently, perturbation realization factors) as building blocks. In particular, we derive sensitivity formulas for two Markov chains with possibly different state spaces. The proposed approach can be used to obtain flexibly the sensitivity formulas for a wide range of problems, including those with partial information. These formulas are the basis for performance optimization of discrete event dynamic systems, including perturbation analysis, Markov decision processes, and reinforcement learning. The approach thus provides insight on on-line learning and performance optimization and opens up new research directions. Sample path based algorithms can be developed.