As Software Engineering enters its new era (SE 3.0), AI coding agents increasingly automate software development workflows. However, it remains unclear how exactly these agents recognize and address software energy concerns-an issue growing in importance due to large-scale data centers, energy-hungry language models, and battery-constrained devices. In this paper, we examined the energy awareness of agent-authored pull requests (PRs) using a publicly available dataset. We identified 216 energy-explicit PRs and conducted a thematic analysis, deriving a taxonomy of energy-aware work. Our further analysis of the applied optimization techniques shows that most align with established research recommendations. Although building and running these agents is highly energy intensive, encouragingly, the results indicate that they exhibit energy awareness when generating software artifacts. However, optimization-related PRs are accepted less frequently than others, largely due to their negative impact on maintainability.
翻译：随着软件工程进入新时代（SE 3.0），AI编程智能体日益自动化软件开发工作流。然而，这些智能体如何具体识别并处理软件能耗问题——这一因大规模数据中心、高能耗语言模型及电池受限设备而日益重要的问题——仍不明确。本文利用公开数据集，对智能体生成的拉取请求（PRs）的能耗意识进行了考察。我们识别出216个显式涉及能耗的PRs，并进行了主题分析，推导出能耗感知工作的分类体系。对所用优化技术的进一步分析表明，大多数技术符合既有研究建议。尽管构建和运行这些智能体本身能耗极高，但令人鼓舞的是，结果表明它们在生成软件制品时展现出能耗意识。然而，与优化相关的PRs被接受频率低于其他类型，这主要源于其对可维护性的负面影响。
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