Behavior support agents can assist a user in reaching their goals by suggesting suitable actions. In order for these agents to be effective, the agent’s advice should be personalized to the user’s needs and preferences. However, the way context influences the user, the internal state of the user and the user’s desired behavior are all subject to change while the agent is in use. If the agent is not able to adapt to these changes, this can lead to a misalignment between the user and the agent. By making the reasoning of the agent explicit, we can allow the user to directly interact with the agent’s user model in order to resolve possible misalignments. We propose to use ordered default logic to reason about the user model as its defeasible nature is inherently well suited to model behavior patterns and routines which may have exceptions dependent on the context. We then analyze different misalignment scenarios and describe how we can use various belief revision techniques to update the agent’s user model and resolve these misalignments.