Adaptive Multi-Agent AI Framework for Real-Time Energy Optimization and Context-Aware Code Review in Software Development

Energy efficiency has become a critical concern in modern software development, particularly for applications deployed in resource-constrained environments such as IoT, mobile devices, and cloud infrastructure. Traditional static analysis tools and single-model code review solutions fail to provide timely, adaptive, and context aware recommendations, leading to suboptimal energy performance and increased technical debt. This paper introduces a novel adaptive multi-agent AI framework that delivers real-time, personalized energy optimization feedback directly within the software development workflow. The proposed system leverages transformer-based code embeddings for semantic analysis, a FAISS-powered contextual memory for historical learning, and a collaborative multi-agent system consisting of specialized AI agents for energy profiling, compliance monitoring, resource allocation, and maintainability assessment. Adaptive reinforcement learning dynamically refines energy recommendations based on developer interactions, ensuring continuous improvement over time. Empirical evaluations demonstrate that the framework effectively reduces redundant feedback, improves system-wide energy efficiency, and enhances developer trust through explainable AI techniques. The results highlight the potential of integrating intelligent, energy-efficient coding practices into modern software engineering workflows, fostering sustainability without compromising performance.