Engineering critical analysis software services: A graph-RAG and self-learning large language model agent services approach

This paper presents Graph-RAG and Self-learning LLM-based Agent Services Framework for structured reasoning and knowledge-driven analysis. The proposed approach integrates graph-enhanced retrieval mechanisms with self-learning Large Language Models (LLMs) to improve critical analysis and domain-specific decision-making. The framework is evaluated using Air Accidents Investigation Branch (AAIB) Publications Reports, which provide structured, investigative narratives aimed at preventing future aviation incidents rather than assigning blame. By leveraging graph-based knowledge learning, the framework enhances causal reasoning, multimodal response generation, and retrieval accuracy, demonstrating its capability to support structured problem analysis based on real-world investigative experiences. Experimental results show significant improvements in hallucination mitigation, retrieval precision, and real-time performance when compared to standard Retrieval-Augmented Generation (RAG) models. The findings highlight the potential of graph-augmented self-learning LLMs in transforming automated analytical workflows, paving the way for enhanced visual knowledge exploration and structured decision support systems.