Although smart tools are increasingly becoming a part of the makerspace culture, their intended goal of supporting human creativity through technology is complicated by the diversity of user abilities, tools, and practices. The modern makerspace is a live environment, ever changing and evolving. If we construct systems using models of activity based on such an environment, it is difficult to maintain those systems over a period of time. I am focusing on understanding methods of augmenting the modern makerspace with smart tools, which aid the user in their work without obstructing in their process. We aim to normalize the use of digitally augmented tools in the creative spaces to enhance the user experience. We explore our design principles by focusing on the data collection process within the makerspace, using machine learning (ML) models to create interactions, and integrating the user in the workflow by making the system adaptable to changes. This is intended to solve the issue of maintainability and repairability of the system. In this WIP talk, I will present our work on two such systems - (1) an audio-based classifier for building and refining models of activity; (2) a tangible avatar to facilitate programming and debugging actions through capacitive touch sensing. Through this talk, I am looking to refine my PhD thesis proposal on how to evaluate the integration of ML models into intelligent systems. Specifically, I am looking to improve the motivation behind my design principles of smart tool maintainability and repairability and tie these into the larger research areas around makerspaces and human-AI interaction.