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.