Hybrid Retrieval-Augmented Generation and Multi-Agent AI Systems for Explainable Enterprise Automation

Abstract

With the increasing use of AI in the business world, there is a strong demand for reliable, knowledgeable and explainable automated systems. Large Language Models (LLMs) could perform language-based processes automatically, but the risks of hallucination, stale knowledge and opaqueness make them less practical in a serious enterprise context. The use of Retrieval-Augmented Generation (RAG) could increase response accuracy by fetching related data from external resources and multi-agent AI systems (MAS) could perform well by means of collaborative reasoning and voting. Our framework, hybrid retrieval-augmented generation and multi-agent AI system (HRAG-MA), aims to deliver explainable enterprise automation. The architecture contains semantic vector and knowledge graph retrieval, intelligent specialists and an explainability system, which together can guide decision-making that is tailored to the given context and also explainable. An executive coordinator controls all the agents and the agents cover planning, retrieval, reasoning, validation and execution activities for improved workflow. The fact-checking in HRAG-MA is improved to eliminate hallucination, and the quality and explainability of AI responses is increased. The proposed system would be an adaptive enterprise automation approach which is also considered to be reliable, usable across several industries and domains including IT, customer services, health, finance and business administration.