# RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detection

arXiv: https://arxiv.org/abs/2603.02345 · HTML: https://mlsanity.com/paper/2603.02345/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 14 of 14)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Where LLM Agents Fail and How They can Learn From Failures](https://mlsanity.com/paper/2509.25370/) (2025)
- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation](https://mlsanity.com/paper/2510.05156/) (2025)
- [Tools Fail: Detecting Silent Errors in Faulty Tools](https://mlsanity.com/paper/2406.19228/) (2024)
- [PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases](https://mlsanity.com/paper/2509.25238/) (2025)
- [Building AI Agents for Autonomous Clouds: Challenges and Design Principles](https://mlsanity.com/paper/2407.12165/) (2024)
- [Where Are The Gaps? A Systematic Mapping Study of Infrastructure as Code Research](https://mlsanity.com/paper/1807.04872/) (2018)
- [Cloud Infrastructure Management in the Age of AI Agents](https://mlsanity.com/paper/2506.12270/) (2025)
- [Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents](https://mlsanity.com/paper/2510.20211/) (2025)
- [An Approach to Checking Correctness for Agentic Systems](https://mlsanity.com/paper/2509.20364/) (2025)
- [Exploring Security Practices in Infrastructure as Code: An Empirical Study](https://mlsanity.com/paper/2308.03952/) (2023)

## Built on by (most cited first, 2 of 2)

- [Skill Drift Is Contract Violation: Proactive Maintenance for LLM Agent Skill Libraries](https://mlsanity.com/paper/2605.10990/) (2026)
- [RSure-Agent: Reliable Use of Tool Observations for Remote Sensing Agents](https://mlsanity.com/paper/2610.04836/) (2026)

## Abstract (arXiv)

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, a verifier agent and a tool generation agent, that collaborate through iterative cross-validation, multi-perspective verification, and tool call history tracking. Evaluation on the AIOpsLab benchmark demonstrates that RIVA, in the presence of erroneous tool responses, recovers task accuracy from 27.3% when using a baseline ReAct agent to 50.0% on average. RIVA also improves task accuracy 28% to 43.8% without erroneous tool responses. Our results show that cross-validation of diverse tool calls enables more reliable autonomous infrastructure verification in production cloud environments.