A Modular Semantic Kernel Agent for Automated Code Review and Refactoring Feedback

In modern software development, maintaining clean, efficient, and reliable code is critical to team productivity and product quality. This paper introduces a modular Large Language Model (LLM)-based agent, designed using Microsoft’s Semantic Kernel framework, for automated code review and refactoring feedback. The agent leverages plugin-based function orchestration, Retrieval-Augmented Generation (RAG), and dynamic prompt engineering to analyze source code across multiple dimensions; including readability, efficiency, security, and adherence to best practices. Integrated into CI/CD pipelines and broader SDLC workflows, the system provides contextual insights, the system provides contextual insights, suggests specific improvements, and explains reasoning for each recommendation. Evaluation results across real-world open-source repositories demonstrate the agent’s effectiveness in reducing human review time while improving refactor quality. The modular design ensures adaptability to various programming languages and enterprise development environments. This research highlights the potential of agentic LLM systems to augment software engineering workflows with intelligent, transparent, and developer-aligned feedback mechanisms. Keywords: Code Review, Semantic Kernel, Plugin Orchestration, Refactoring, Large Language Models, Agentic AI, Retrieval-Augmented Generation, Prompt Engineering