This paper presents a novel approach to improving software quality and efficiency using a Large Language Model (LLM)-based AI agent for code review. The model is trained on extensive code repositories, including code reviews, bug reports, and best practices documentation. It aims to detect code smells, identify potential bugs, and provide suggestions for improvement, enhancing code quality and developer education. Unlike traditional static analysis tools, the LLM-based AI agent can predict future risks and offer proactive suggestions for optimization. The model's effectiveness is demonstrated through its ability to reduce post-release bugs and improve code review processes, as evidenced by developer sentiment towards LLM feedback. The study introduces four specialized agents: Code Review, Bug Report, Code Smell, and Code Optimization. Each agent is trained on a large dataset of code repositories to identify issues and suggest improvements. The Code Review Agent analyzes code for potential issues, the Bug Report Agent identifies bugs, the Code Smell Agent detects code smells, and the Code Optimization Agent provides optimization suggestions. The model's preliminary results show its capability in identifying various issues across different programming languages and AI domains, offering actionable recommendations for code improvement. Future work includes evaluating the accuracy and efficiency of LLM-generated documentation compared to manual methods. This will involve an empirical study of code reviews and best practice documentation, alongside insights from developer discussions. The goal is to refine the model's accuracy and highlight its potential in streamlining software development processes through proactive code improvement and education. The study concludes that LLM-based AI agents can significantly enhance code review processes, improve code quality, and contribute to developer education, paving the way for more efficient and knowledge-driven software development.This paper presents a novel approach to improving software quality and efficiency using a Large Language Model (LLM)-based AI agent for code review. The model is trained on extensive code repositories, including code reviews, bug reports, and best practices documentation. It aims to detect code smells, identify potential bugs, and provide suggestions for improvement, enhancing code quality and developer education. Unlike traditional static analysis tools, the LLM-based AI agent can predict future risks and offer proactive suggestions for optimization. The model's effectiveness is demonstrated through its ability to reduce post-release bugs and improve code review processes, as evidenced by developer sentiment towards LLM feedback. The study introduces four specialized agents: Code Review, Bug Report, Code Smell, and Code Optimization. Each agent is trained on a large dataset of code repositories to identify issues and suggest improvements. The Code Review Agent analyzes code for potential issues, the Bug Report Agent identifies bugs, the Code Smell Agent detects code smells, and the Code Optimization Agent provides optimization suggestions. The model's preliminary results show its capability in identifying various issues across different programming languages and AI domains, offering actionable recommendations for code improvement. Future work includes evaluating the accuracy and efficiency of LLM-generated documentation compared to manual methods. This will involve an empirical study of code reviews and best practice documentation, alongside insights from developer discussions. The goal is to refine the model's accuracy and highlight its potential in streamlining software development processes through proactive code improvement and education. The study concludes that LLM-based AI agents can significantly enhance code review processes, improve code quality, and contribute to developer education, paving the way for more efficient and knowledge-driven software development.