# Cloud Infrastructure Management in the Age of AI Agents

arXiv: https://arxiv.org/abs/2506.12270 · HTML: https://mlsanity.com/paper/2506.12270/

## Key facts

- Cited by 9 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 12 of its 33 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Placed in

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

## Builds on (most cited first, 12 of 12)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents](https://mlsanity.com/paper/2406.03007/) (2024)
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [Building AI Agents for Autonomous Clouds: Challenges and Design Principles](https://mlsanity.com/paper/2407.12165/) (2024)
- [Multi-Programming Language Ensemble for Code Generation in Large Language Model](https://mlsanity.com/paper/2409.04114/) (2024)

## Built on by (most cited first, 9 of 9)

- [A Survey on Agentic Security: Applications, Threats and Defenses](https://mlsanity.com/paper/2510.06445/) (2025)
- [Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents](https://mlsanity.com/paper/2510.20211/) (2025)
- [AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression](https://mlsanity.com/paper/2512.13956/) (2025)
- [IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection](https://mlsanity.com/paper/2512.14792/) (2025)
- [RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detection](https://mlsanity.com/paper/2603.02345/) (2026)
- [Synergy: A Next-Generation General-Purpose Agent for Open Agentic Web](https://mlsanity.com/paper/2603.28428/) (2026)
- [Interactive Clarification for Cloud Infrastructure-as-Code Synthesis](https://mlsanity.com/paper/2604.02382/) (2026)
- [Automated Synthesis of Cloud Emulators](https://mlsanity.com/paper/2608.23842/) (2026)
- [Towards Agentic Cloud Engineering: Graph and Loop Engineering with a Zero-Trust Agent Harness](https://mlsanity.com/paper/2609.00050/) (2026)

## Abstract (arXiv)

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.