In autonomous experimental setups, replicating burdensome human-intensive tasks, such as material sufficiency management or equipment cleaning, is challenging . Simultaneously, these ensure the reliability and fidelity of measurements and require rigorous validation for experimental accuracy and consideration of potential hardware and software flaws . The integration of ML and computer vision methods, as proposed in recent studies , can significantly improve system robustness and is crucial to monitor parameter validity in real time . The goal is to achieve data integrity and support AI planners in informed decision-making. This is particular beneficial for non-aqueous battery systems experimentation in gloveboxes, where assembling and controlling multiple experiments present complexities that even with human intervention may prove challenging to manage effectively. These developments, thus, signify a transformative shift in scientific experimentation towards enhanced precision and reliability , highlighted by the critical need to recognize and address the technical challenges in experiment-specific boundaries to ensure results are both comprehensible and reproducible .