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\title{Enhancing Generative AI Reliability via AI~ Agent Workflows in
6G-Enabled Edge Computing~~~~~~~~~~~}



\author[1]{Laha Ale}%
\author[2]{Scott A. King}%
\author[2]{Ning Zhang}%
\author[2]{Huanlai Xing}%
\affil[1]{School of Computing and Artificial Intelligence, Southwest Jiaotong University}%
\affil[2]{Affiliation not available}%


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  \date{April 24, 2026}


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\begin{abstract}
Generative artificial intelligence (GenAI), particularly large language
models (LLMs), has revolutionized various applications by producing
coherent and contextually relevant text. However, despite their
advancements, LLMs are prone to hallucinations, instances where the AI
generates inaccurate or fabricated information. Retrieval-augmented
generation (RAG) has emerged as a technique to enhance GenAI by
integrating external knowledge sources beyond the model's training data.
While RAG improves factual grounding, it alone cannot fully eliminate
hallucinations. To address this limitation, agentic workflows
incorporating external tools such as APIs, search engines, and
self-reflective mechanisms offer a promising solution. These workflows
enable models to iteratively assess and refine their outputs, thereby
reducing errors and enhancing factual accuracy. This paper presents a
novel framework that combines agentic workflows with RAG within 6G
networks to achieve more reliable generative AI by deploying autonomous
agents that reflect on outputs and leverage real-time knowledge from
external sources to improve response quality and accuracy. We explore
the deployment of these workflows in 6G-enabled edge environments,
facilitating scalable, real-time knowledge integration and model
refinement. Our framework addresses current limitations in RAG-enhanced
services by utilizing 6G edge intelligence for data fusion, dynamic
knowledge base updates, and customizable AI service delivery. Through a
multi-agent system comprising generator and critic agents, we
effectively reduce hallucinations via iterative self-criticism, paving
the way for more reliable and accurate generative AI services across
diverse applications.%
\end{abstract}%



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