Managing Linux servers with LLM-based AI agents: An empirical evaluation with GPT4
Qing Cao, Feiyi Wang, Lisa C. Lindley, Zejiang Wang
Abstract
This paper presents an empirical study on the application of Large Language Model (LLM)-based AI agents for automating server management tasks in Linux environments. We aim to evaluate the effectiveness, efficiency, and adaptability of LLM-based AI agents in handling a wide range of server management tasks, and to identify the potential benefits and challenges of employing such agents in real-world scenarios. We present an empirical study where a GPT-based AI agent autonomously executes 150 unique tasks across 9 categories, ranging from file management to editing to program compilations. The agent operates in a Dockerized Linux sandbox, interpreting task descriptions and generating appropriate commands or scripts. Our findings reveal the agent’s proficiency in executing tasks autonomously and adapting to feedback, demonstrating the potential of LLMs in simplifying complex server management for users with varying technical expertise. This study contributes to the understanding of LLM applications in server management scenarios, and paves the foundation for future research in this domain.