paper-with-me

Papers

Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents

2025-10-23 · Zhenning Yang, Hui Guan, Victor Nicolet, Brandon Paulsen, Joey Dodds, Daniel Kroening, Ang Chen 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CLIs, or SDKs, it loses visibility into external changes, causing infrastructure drift, where the configuration becomes outdated, and later IaC operations may undo valid updates or trigger errors.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$\times$ improvement).

📄 PDF Abstract BibTeX arXiv:2510.20211

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Cloud Infrastructure Management in the Age of AI Agents

2025-06-13 · Zhenning Yang, Archit Bhatnagar, Yiming Qiu, Tongyuan Miao 외

Cloud infrastructure is the cornerstone of the modern IT industry. However, managing this infrastructure effectively requires considerable manual effort from the DevOps engineering team. We make a case for developing AI …

Management

ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm

2026-07-11 · Kefan Song, Yanjun Qi arxiv

Autonomous CLI agents can now execute hundreds of actions across multi-hour sessions: writing code, executing shell commands, browsing the web, and managing cloud infrastructure, all with minimal human oversight. Does gr…

Governing Cloud Data Pipelines with Agentic AI

2025-12-24 · Aswathnarayan Muthukrishnan Kirubakaran, Adithya Parthasarathy, Nitin Saksena, Ram Sekhar Bodala 외 arxiv

Cloud data pipelines increasingly operate under dynamic workloads, evolving schemas, cost constraints, and strict governance requirements. Despite advances in cloud-native orchestration frameworks, most production pipeli…

Belief-Desire-Intention (BDI) Multi-agent System for Cloud Marketplace Negotiation

2022-06-16 · Saurabh Deochake

With the evolution of cloud computing, there has been a rise of large enterprises extending their infrastructure and workloads into the public cloud. This paper proposes a full-fledged framework for a Belief-Desire-Inten…

Cloud ComputingDecision Making

Model-Free Model Reconciliation

2019-03-17 · Sarath Sreedharan, Alberto Olmo, Aditya Prasad Mishra, Subbarao Kambhampati

Designing agents capable of explaining complex sequential decisions remain a significant open problem in automated decision-making. Recently, there has been a lot of interest in developing approaches for generating such …

Decision MakingmodelPhilosophy