This demo shows that policy graphs (PGs) provide reliable explanations of the behaviour of agents trained in two distinct environments. Additionally, this work shows the ability to generate surrogate agents using PGs that exhibit accurate behavioral resemblances to the original agents and that this feature allows us to validate the explanations given by the system. This facilitates transparent integration of opaque agents into socio-technical systems, ensuring explainability of their actions and decisions, enabling trust in hybrid human-AI environments, and ensuring cooperative efficacy. We present demonstrations based on two environments and we present a work-in-progress library that will allow integration with a broader range of environments and types of agent policies.