Imagine a world where software never crashes due to unexpected errors. That's the promise of exception handling, a critical yet often overlooked aspect of robust coding. Unfortunately, even seasoned developers can struggle to implement perfect exception handling, leading to fragile code susceptible to crashes and vulnerabilities. Now, a new AI-powered framework called Seeker is changing the game. Seeker acts like a team of specialized code agents, each with a unique role in identifying and neutralizing potential exceptions. One agent, the 'Scanner,' meticulously combs through the code, dividing it into manageable chunks. The 'Detector' then steps in, using static analysis and scenario matching to pinpoint vulnerable areas. Next, the 'Predator' consults a vast library of exception knowledge called the Common Exception Enumeration (CEE) to understand the potential risks. The 'Ranker' prioritizes these risks based on their likelihood and potential impact, and finally, the 'Handler' generates optimized code with robust exception handling strategies. Seeker's secret weapon is its use of intermediate language (IL) and a clever algorithm called Deep Retrieval-Augmented Generation (Deep-RAG). This allows it to navigate the complex inheritance relationships of exception types, ensuring the most specific and effective handling. Tests show that Seeker dramatically improves the robustness of code generated by large language models (LLMs) like those powering GitHub Copilot. It significantly outperforms existing methods, achieving higher accuracy in both detecting and handling exceptions. Furthermore, Seeker exhibits remarkable stability over time and across different code complexities. While the current version focuses on Java, the framework's adaptable design allows for expansion to other languages like Python and C#. The future of exception handling looks bright, with Seeker paving the way for more robust, reliable, and ultimately, crash-free software.