Smart-Split: Ai-Driven Context-Aware System Decomposition For Small And Medium-Sized Businesses

Abstract

The transition from monolithic to microservices architecture has become essential for software modernization, yet small and medium-sized enterprises (SMEs) face significant barriers, including prohibitively expensive commercial tools, resource-intensive processes, and context-unaware decomposition approaches. Existing solutions like IBM Mono2Micro and AWS Microservice Extractor rely primarily on static analysis, overlooking critical runtime behavior patterns and domain knowledge, resulting in suboptimal service boundaries misaligned with business capabilities. This research proposes SMART-Split, a resource-efficient multi-agent Retrieval-Augmented Generation (RAG) framework for automated monolith decomposition, specifically designed for Go applications under 50,000 lines of code. The framework employs specialized agents—Static Analyzer, Runtime Profiler, Domain Knowledge Agent, and Decomposer Agent coordinated through a supervisor pattern to integrate multiple analysis perspectives. By combining Abstract Syntax Tree analysis, runtime execution traces, and domain knowledge extraction through RAG, SMART-Split addresses critical gaps in existing decomposition tools. The framework introduces three key innovations: (1) a multi-agent collaborative architecture that synthesizes static, dynamic, and domain context; (2) a lightweight RAG implementation optimized for resourceconstrained environments; and (3) a hybrid decomposition algorithm that produces business-aligned service boundaries. Validation across three open-source Go monoliths demonstrates improved decomposition quality through metrics including Modularity Quality (MQ > 0.7), Service Independence Score (SIS > 0.8), and Business Alignment Index (BAI > 0.9). Results indicate SMART-Split achieves comparable decomposition quality to commercial tools while requiring significantly fewer computational resources, making microservices modernization accessible and affordable for SMEs.