Free-form Game-Design (GD) environments show promise in fostering Computational Thinking (CT) skills at a young age. However, such environments can be challenging to some students due to their highly open-ended nature. Our long-term goal is to alleviate this difficulty via pedagogical agents that can monitor the student interaction with the environment, detect when the student needs help and provide personalized support accordingly. In this paper, we present a preliminary evaluation of one such agent deployed in a real-word free-form GD learning environment to foster CT in the early K-12 education, Unity-CT. We focus on the effect of repetition by comparing student behaviors between no intervention, 1-shot intervention, and repeated intervention groups for two different errors that are known to be challenging in the online lessons of Unity-CT environment. Our findings showed that the agent was perceived very positively by the students and the repeated intervention showed promising results in terms of helping students make less errors and more correct behaviors, albeit only for one of the two target errors. Based on these results, we provide insights on how to improve the delivery of the agent's interventions in free-form GD environments.