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The most common representation formalisms for planning are descriptive models. They abstractly describe what the actions do and are tailored for efficiently computing the next state(s) in a state transition system. But acting requires operational models that describe how to do things, with rich control structures for closed-loop online decision-making. Using descriptive representations for planning and operational representations for acting can lead to problems with developing and verifying consistency of the different models. We define and implement an integrated acting-and-planning system in which both planning and acting use the same operational models, which are written in a general-purpose hierarchical task-oriented language offering rich control structures. The acting component is inspired by the well-known PRS system, except that instead of being purely reactive, it can get advice from the planner. Our planning algorithm, RAEplan, plans by doing Monte Carlo rollout simulations of the actor’s operational models. Our experiments show significant benefits in the efficiency of the acting and planning system.