[2603.02345] RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detection

Computer Science > Software Engineering arXiv:2603.02345 (cs) [Submitted on 2 Mar 2026] Title:RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detection Authors:Sami Abuzakuk, Lucas Crijns, Anne-Marie Kermarrec, Rafael Pires, Martijn de Vos View a PDF of the paper titled RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detection, by Sami Abuzakuk and 4 other authors View PDF HTML (experimental) Abstract:Infrastructure as code (IaC) tools automate cloud provisioning but verifying that deployed systems remain consistent with the IaC specifications remains challenging. Such configuration drift occurs because of bugs in the IaC specification, manual changes, or system updates. Large language model (LLM)-based agentic AI systems can automate the analysis of large volumes of telemetry data, making them suitable for the detection of configuration drift. However, existing agentic systems implicitly assume that the tools they invoke always return correct outputs, making them vulnerable to erroneous tool responses. Since agents cannot distinguish whether an anomalous tool output reflects a real infrastructure problem or a broken tool, such errors may cause missed drift or false alarms, reducing reliability precisely when it is most needed. We introduce RIVA (Robust Infrastructure by Verification Agents), a novel multi-agent system that performs robust IaC verification even when tools produce incorrect or misleading outputs. RIVA employs two specialized agents...