Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents
Not on the map. Why?
Abstract (arXiv)
Cloud infrastructure is managed through a mix of interfaces -- traditionally, cloud consoles, command-line interfaces (CLI), and SDKs are the tools of choice. Recently, Infrastructure-as-Code/IaC frameworks (e.g., Terraform) have quickly gained popularity. Unlike conventional tools, IaC~frameworks encode the infrastructure in a "source-of-truth" configuration. They are capable of automatically carrying out modifications to the cloud -- deploying, updating, or destroying resources -- to bring the actual infrastructure into alignment with the IaC configuration. However, when IaC is used alongside consoles, CLIs, or SDKs, it loses visibility into external changes, causing infrastructure drift, where the configuration becomes outdated, and later IaC operations may undo valid updates or trigger errors. We present NSync, an automated system for IaC reconciliation that propagates out-of-band changes back into the IaC program. Our key insight is that infrastructure changes eventually all occur via cloud API invocations -- the lowest layer for cloud management operations. NSync gleans insights from API traces to detect drift (i.e., non-IaC changes) and reconcile it (i.e., update the IaC configuration to capture the changes). It employs an agentic architecture that leverages LLMs to infer high-level intents from noisy API sequences, synthesize targeted IaC updates using specialized tools, and continually improve through a self-evolving knowledge base of past reconciliations. We further introduce a novel evaluation pipeline for injecting realistic drifts into cloud infrastructure and assessing reconciliation performance. Experiments across five real-world Terraform projects and 372 drift scenarios show that NSync outperforms the baseline both in terms of accuracy (from 0.71 to 0.97 pass@3) and token efficiency (1.47× improvement).
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 72 references that are in the map. First posted on arXiv 23 Oct 2025.
Builds on
16 of 72 references.
References · 16
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 1,002
- 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 421
- 2022CodeT: Code Generation with Generated Testscited by 384
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 232
- 2025Agent-R: Training Language Model Agents to Reflect via Iterative Self-Trainingcited by 82
- 2024TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Taskscited by 75
- 2023RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repaircited by 50
4 more
- 2025AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Cloudscited by 40
- 2025A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applicationscited by 25
- 2025Towards Effectively Leveraging Execution Traces for Program Repair with Code LLMscited by 11
- 2025Cloud Infrastructure Management in the Age of AI Agentscited by 9
Built on by
Citing papers per year: 2026: 5.
5 papers.
- 2026RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detectioncited by 2
- 2026Interactive Clarification for Cloud Infrastructure-as-Code Synthesiscited by 0
- 2026TerraRepair: A Tool-Grounded LLM Agent for Infrastructure-as-Code Repaircited by 0
- 2026Towards a Systems Foundation for Agentic Cloud Managementcited by 0
- 2026Automated Synthesis of Cloud Emulatorscited by 0
- 2026Automated Synthesis of Cloud Emulators24 Aug 2026
- 2026Towards a Systems Foundation for Agentic Cloud Management28 Jul 2026
- 2026TerraRepair: A Tool-Grounded LLM Agent for Infrastructure-as-Code Repair13 Jul 2026
- 2026Interactive Clarification for Cloud Infrastructure-as-Code Synthesis1 Apr 2026
- 2026RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detection2 Mar 2026
Similar, not cited
Close in wording, no citation either way
- 2026SWE-InfraBench: Evaluating Language Models on Cloud Infrastructure Code ↗
- 2025Multi-Agent Code-Orchestrated Generation for Reliable Infrastructure-as-Code
- 2025IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection
- 2026TerraFormer: Automated Infrastructure-as-Code with LLMs Fine-Tuned via Policy-Guided Verifier Feedback
- 2026Verifier-First Evaluation of Agentic LLMs for Infrastructure-as-Code Generation
- 2026Mono2Sls: Automated Monolith-to-Serverless Migration via Multi-Stage Pipeline with Static Analysis ↗
- 2024Building AI Agents for Autonomous Clouds: Challenges and Design Principles
- 2024Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight