DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle
Abstract
Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents' capabilities in the full software DevOps cycle are still unknown. Different from pure code generation, handling the DevOps cycle in real-world software, including developing, deploying, and managing, requires analyzing large-scale projects, understanding dynamic program behaviors, leveraging domain-specific tools, and making sequential decisions. However, existing benchmarks focus on isolated problems and lack environments and tool interfaces for DevOps. We introduce DevOps-Gym, the first end-to-end benchmark for evaluating AI agents across core DevOps workflows: build and configuration, monitoring, issue resolving, and test generation. DevOps-Gym includes 700+ real-world tasks collected from 30+ projects in Java and Go. We develop a semi-automated data collection mechanism with rigorous and non-trivial expert efforts in ensuring the task coverage and quality. Our evaluation of state-of-the-art models and agents reveals fundamental limitations: they struggle with issue resolving and test generation in Java and Go, and remain unable to handle new tasks such as monitoring and build and configuration. These results highlight the need for essential research in automating the full DevOps cycle with AI agents.
Cite
@article{arxiv.2601.20882,
title = {DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle},
author = {Yuheng Tang and Kaijie Zhu and Bonan Ruan and Chuqi Zhang and Michael Yang and Hongwei Li and Suyue Guo and Tianneng Shi and Zekun Li and Christopher Kruegel and Giovanni Vigna and Dawn Song and William Yang Wang and Lun Wang and Yangruibo Ding and Zhenkai Liang and Wenbo Guo},
journal= {arXiv preprint arXiv:2601.20882},
year = {2026}
}