Toward Trustworthy Digital Twins in Agentic AI-based Wireless Network Optimization: Challenges, Solutions, and Opportunities

Abstract

Optimizing modern wireless networks is exceptionally challenging due to their high dynamism and complexity. While the agentic artificial intelligence (AI) powered by reinforcement learning (RL) offers a promising solution, its practical application is limited by prohibitive exploration costs and potential risks in the real world. The emerging digital twin (DT) technology provides a safe and controlled virtual environment for agentic AI training, but its effectiveness critically depends on the DT's fidelity. Policies trained in a low-fidelity DT that does not accurately represent the physical network may experience severe performance degradation upon real-world deployment. In this article, we introduce a unified DT evaluation framework to ensure trustworthy DTs in agentic AI-based network optimization. This evaluation framework shifts from conventional isolated physical accuracy metrics, such as wireless channel and user trajectory similarities, to a more holistic, task-centric DT assessment. We demonstrate it as an effective guideline for design, selection, and lifecycle management of wireless network DTs. A comprehensive case study on a real-world wireless network testbed shows how this evaluation framework is used to pre-filter candidate DTs, leading to a significant reduction in training and testing costs without sacrificing deployment performance. Finally, potential research opportunities are discussed.