Imagine a world where AI writes code, freeing human developers from tedious tasks. We're getting closer, but AI-generated code often contains subtle errors that require manual debugging. Existing AI debugging tools treat programs as single blocks, overlooking the layered nature of code, from syntax to algorithms. Researchers have introduced a new approach, the "Multi-Granularity Debugger" (MGDebugger), which uses a hierarchical method, similar to how human developers break down complex problems. MGDebugger dissects code into smaller, manageable sub-functions, forming a tree-like structure. This allows the AI to isolate bugs at different levels, debugging from the bottom up. The system also uses a simulated Python executor, powered by a language model, to track variables and pinpoint errors without relying on external tools. Tested on benchmarks like HumanEval and MBPP, MGDebugger significantly outperforms current methods. With one language model, it achieves a remarkable 94.5% accuracy on HumanEval and a high repair success rate on others. Notably, MGDebugger is particularly adept at handling complex logical errors and lengthy code—areas where other methods struggle. Looking ahead, researchers envision MGDebugger evolving to handle larger projects and integrating with self-training systems to further refine AI code generation. This hierarchical debugging approach brings us closer to a future where AI not only writes code but also debugs and perfects it, making coding more efficient and accessible.