We address the problem of representing and verifying the behavior of an agent following a policy in dynamic environments. Our focus is on policies that yield sequences of actions, according to the present knowledge in the state, with the aim of reaching some main goal. We distinguish certain cases where the dynamic nature of the environment may require the agent to stop and revise its next actions. We employ the notion of maintenance to check whether a given policy can maintain the conditions of the main goal, given a respite from environment actions. Furthermore, we apply state clustering to mitigate the large state spaces caused by having irrelevant information in the states, and under some conditions this clustering might change the worst-case complexity. By preserving the behavior of the policy, it helps in checking for maintenance with a guarantee that the result also holds in the original system.