# How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study

arXiv: https://arxiv.org/abs/2512.24636 · HTML: https://mlsanity.com/paper/2512.24636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 31 Dec 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, 7 of 7)

- [Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap](https://mlsanity.com/paper/2410.06107/) (2024)
- [Catalog of Energy Patterns for Mobile Applications](https://mlsanity.com/paper/1901.03302/) (2019)
- [A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama](https://mlsanity.com/paper/2405.03616/) (2024)
- [Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards](https://mlsanity.com/paper/2007.09976/) (2020)
- [Do Energy-oriented Changes Hinder Maintainability?](https://mlsanity.com/paper/1908.08332/) (2019)
- [Exploring Challenges in Test Mocking: Developer Questions and Insights from StackOverflow](https://mlsanity.com/paper/2505.08300/) (2025)
- [The Influence of the Java Collection Framework on Overall Energy Consumption](https://mlsanity.com/paper/1602.00984/) (2016)

## Abstract (arXiv)

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.