AI-enhanced debugging offers a transformative approach to software development by leveraging tools like Claude, Cursor, and Microsoft's debug-gym. These AI tools streamline the debugging process through three main levels: "lazy" AI debugging, which involves inputting error messages into large language models (LLMs) for quick fixes; structured prompting, which enhances AI's efficacy by providing context-rich bug reports; and interactive debugging agents, which autonomously explore code execution to identify and rectify issues. While current methods like "lazy prompting" and structured prompts focus on efficient error diagnosis, future developments in AI debugging agents promise a more autonomous approach, where AI can actively interact with code, set breakpoints, and test solutions in real-time. These advancements not only improve error resolution but also offer developers valuable learning opportunities by elucidating the underlying causes of bugs, thus enriching their engineering skills. As AI debugging tools continue to evolve, they are expected to handle the more tedious aspects of bug tracking, allowing developers to focus on creative challenges while maintaining oversight to ensure quality and accuracy in AI-generated solutions.