Cloud Infrastructure Management in the Age of AI Agents
Zhenning Yang, Archit Bhatnagar, Yiming Qiu, Tongyuan Miao, Patrick Tser Jern Kon, Yunming Xiao, Yibo Huang, Martin Casado, Ang Chen

TL;DR
This paper explores the potential of AI agents powered by large language models to automate cloud infrastructure management tasks across various interfaces, aiming to reduce manual effort and improve efficiency.
Contribution
It presents a preliminary study on using LLM-powered AI agents for cloud management, highlighting interface effectiveness and identifying key research challenges.
Findings
AI agents can effectively interact with SDK, CLI, IaC, and web portals.
Different interfaces have varying effectiveness for specific management tasks.
The study identifies key challenges and potential solutions for AI-driven cloud management.
Abstract
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 agents powered by large language models (LLMs) to automate cloud infrastructure management tasks. In a preliminary study, we investigate the potential for AI agents to use different cloud/user interfaces such as software development kits (SDK), command line interfaces (CLI), Infrastructure-as-Code (IaC) platforms, and web portals. We report takeaways on their effectiveness on different management tasks, and identify research challenges and potential solutions.
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Taxonomy
TopicsBig Data and Business Intelligence · Network Security and Intrusion Detection
