This paper introduces a novel approach to enhancing software quality and efficiency through a Large Language Model (LLM)-based AI agent designed for code review. The LLM-based model is trained on extensive code repositories, including code reviews, bug reports, and best practices documentation. Unlike traditional static code analysis tools, this AI agent can predict future potential risks, detect code smells, identify bugs, and provide actionable suggestions for improvement. The model aims to optimize code, enhance developer education, and reduce post-release bugs. The authors present preliminary results from a study of 10 AI-based projects, demonstrating the model's effectiveness in identifying and addressing various issues. Future work will focus on evaluating the accuracy and efficiency of LLM-generated documentation compared to manual methods, further validating the model's potential in streamlining software development processes and enhancing developer education. The paper concludes by highlighting the transformative potential of LLM technology in software development, emphasizing its role in improving code quality, efficiency, and developer knowledge.This paper introduces a novel approach to enhancing software quality and efficiency through a Large Language Model (LLM)-based AI agent designed for code review. The LLM-based model is trained on extensive code repositories, including code reviews, bug reports, and best practices documentation. Unlike traditional static code analysis tools, this AI agent can predict future potential risks, detect code smells, identify bugs, and provide actionable suggestions for improvement. The model aims to optimize code, enhance developer education, and reduce post-release bugs. The authors present preliminary results from a study of 10 AI-based projects, demonstrating the model's effectiveness in identifying and addressing various issues. Future work will focus on evaluating the accuracy and efficiency of LLM-generated documentation compared to manual methods, further validating the model's potential in streamlining software development processes and enhancing developer education. The paper concludes by highlighting the transformative potential of LLM technology in software development, emphasizing its role in improving code quality, efficiency, and developer knowledge.