Can Agentic AI Undermine Its Own Assurance Loop? Closed-Loop Dependency Assurance for Cognitive Network Digital Twins
Ahmed Hussain, Konstantinos Kalogiannis, Salahuddin Salahuddin, Hexu Li, Thiemo Voigt
Abstract
Agentic Artificial Intelligence (AI) and Network Digital Twins (NDTs) are increasingly combined to form Cognitive Digital Twins (CDTs) that translate operational intents into network changes and support closed-loop management. However, a network change may achieve its intended objective while disrupting the monitoring or recovery mechanisms needed for subsequent decisions. For example, a service may be rerouted while its latency-monitoring traffic continues along the original path, producing measurements that no longer reflect the service's performance. We propose Closed-Loop Dependency Assurance (CLDA), an assurance mechanism that augments agentic AI-enabled NDTs with dependency-aware validation. In our framework, an agentic AI planner generates and refines network plans, while the NDT evaluates the projected network outcomes of these plans. CLDA extends this analysis using a Loop Dependency Graph (LDG) that connects service requirements to monitoring mechanisms, forwarding paths, failure domains, and recovery prerequisites. CLDA projects candidate actions onto these dependencies, checks intermediate states of multistep plans, and supports deterministic make-before-break repairs using temporary monitoring mechanisms. Unlike the Large Language Model (LLM)-based planner, CLDA uses explicit, deterministic rules for assurance decisions and repair. We evaluate CLDA through controlled reconfigurations, a 240-trial LLM-based planning campaign, and live network execution. A separate synthetic study examines deterministic assurance scalability across network models representing up to 1,024 routers.