Imagine a world where finding and fixing software bugs is no longer a tedious, time-consuming chore. Researchers are exploring how Large Language Models (LLMs), the brains behind AI chatbots, could revolutionize the way we debug. Traditionally, developers have used methods like Spectrum-Based Fault Localization (SBFL), which rely on statistical analysis of test results. However, SBFL can be inaccurate. Learning-based techniques are emerging but need tons of training data. LLMs offer a promising alternative, leveraging their code comprehension abilities. But even LLMs hit roadblocks when dealing with huge codebases, token limitations, and intricate software systems. Enter LLM4FL, a new approach that blends the best of both worlds. LLM4FL tackles the challenge of massive codebases by using a divide-and-conquer approach, breaking down the code into bite-sized chunks that LLMs can handle. It also employs a clever team of two LLM agents: a "Tester" and a "Debugger." The Tester, like a detective, analyzes failing tests and stack traces to identify suspicious code sections. The Debugger then steps in, meticulously examining those sections to pinpoint the root cause of the problem, acting like a surgeon. This back-and-forth process, facilitated by prompt chaining, mimics how human developers often collaborate during debugging. The researchers tested LLM4FL against real-world bugs from open-source Java projects and the results are impressive. LLM4FL significantly outperformed existing LLM-based methods and even beat out some cutting-edge learning-based techniques that require extensive training. Turns out, giving LLMs the right information in the right order significantly impacts their effectiveness. This opens up new avenues for research into how to best structure code analysis for LLMs, and hints at a future where AI-assisted debugging becomes the norm.