In this paper, we compute general smoothing dynamics for partially observed dynamical systems generating Poisson observations. We consider two model classes, each Markov modulated Poisson processes, whose stochastic intensities depend upon the state of an unobserved Markov process. In one model class, the hidden state process is a continuously-valued Ito process, which gives rise to a continuous sample-path stochastic intensity. In the other model class, the hidden state process is a continuous-time Markov chain, giving rise to a pure jump stochastic intensity. To compute filtered estimates of state process, we establish dynamics, whose solutions are unnormalized marginal probabilities; however, these dynamics include Lebesgue-Stieltjes stochastic integrals. By adapting the transformation techniques introduced by J. M. C. Clark, we compute filter dynamics which do not include these stochastic integrals. To construct smoothers, we exploit a duality between our forward and backward transformed dynamics and thereby completely avoid the technical complexities of backward evolving stochastic integral equations. The general smoother dynamics we present can readily be applied to specific smoothing algorithms, referred to in the literature as: Fixed point smoothing, fixed lag smoothing and fixed interval smoothing. It is shown that there is a clear motivation to compute smoothers via transformation techniques similar to those presented by J. M. C. Clark, that is, our smoothers are easily obtained without recourse to two sided stochastic integration. A computer simulation is included.