A new interpretability toolkit for AI agents enhances the ability to diagnose tool-use failures by analyzing internal model states before actions are taken. This approach aims to improve the reliability of AI in high-stakes environments by providing deeper insights into decision-making processes, particularly in long-horizon tasks.