An LLM-Based Autonomous Agent for Network Diagnosis and Recovery on a Digital Twin

Abstract

Operating complex communication networks (NWs) requires rapid, reliable failure recovery, yet fully autonomous recovery in realistic settings remains elusive. We present a failure-recovery-specialized LLM agent that encodes the operator’s workflow into a structured reasoning-and-acting process. A core design is stage-constrained operation, which regulates tool admissibility by operational stage to prevent unsafe actions and maintain closed-loop stability. The agent interacts with a device-accurate NW digital twin built on Cisco Modeling Labs via diagnostic and control tool calls under these constraints. In evaluations on diverse interface (IF) and link failure patterns, the framework enabled the tested commercial LLMs to autonomously recover most failures, with frontier-class models achieving full recovery in every setting. Augmenting diagnostic outputs with baseline diffs improved accuracy across all models, and command-aware retrieval-augmented generation (RAG) further raised success for cost-effective models by supplying proven precedents. Case studies further show fully autonomous handling of complex failures such as IF flapping and a configuration-induced routing loop, demonstrating that a structured agent framework can bridge LLM reasoning and the demands of practical NW failure recovery.