mlsanityData to 14 Oct 2026

Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents

arXiv 2510.20211 ↗First posted 23 Oct 2025cs.SE cs.AI cs.LGcited by 5

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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CLIs, or SDKs, it loses visibility into external changes, causing infrastructure drift, where the configuration becomes outdated, and later IaC operations may undo valid updates or trigger errors.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

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
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4 more
  1. 2025AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Cloudscited by 40
  2. 2025A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applicationscited by 25
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  4. 2025Cloud Infrastructure Management in the Age of AI Agentscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026SWE-InfraBench: Evaluating Language Models on Cloud Infrastructure Code ↗
  2. 2025Multi-Agent Code-Orchestrated Generation for Reliable Infrastructure-as-Code
  3. 2025IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection
  4. 2026TerraFormer: Automated Infrastructure-as-Code with LLMs Fine-Tuned via Policy-Guided Verifier Feedback
  5. 2026Verifier-First Evaluation of Agentic LLMs for Infrastructure-as-Code Generation
  6. 2026Mono2Sls: Automated Monolith-to-Serverless Migration via Multi-Stage Pipeline with Static Analysis ↗
  7. 2024Building AI Agents for Autonomous Clouds: Challenges and Design Principles
  8. 2024Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight