In this short experience paper, we present Pomona, a lightweight agentic tool that utilises agent skills for continuous automated code quality improvement. Inspired by the philosophy of Kaizen(TM), Pomona automates a cycle of discovery and incremental repair: a Scanning skill identifies improvement tasks (e.g., linting violations, technical debt markers, and test gaps) and prioritises them in a structured backlog, while a Repair skill generates tiny pull requests (PRs) targeting ~10 lines of diff. This human-in-the-loop design enables frequent, low-risk improvements while maintaining engineer trust and productivity and reducing technical debt. We evaluated Pomona through a one-month deployment in a team and a questionnaire distributed to 10 senior engineers. Our preliminary results are promising: 15 of 17 generated PRs were successfully merged with a median time-to-close of under 2 hours. Furthermore, 8/10 of surveyed engineers expressed a desire to adopt Pomona, praising small diff sizes and Pomona's focus on improving code quality. We conclude by discussing actionable insights for researchers and practitioners on strategies for effective agentic deployment in industry.

翻译：在这篇短篇经验报告中，我们介绍了Pomona，一种轻量级智能体工具，它利用智能体技能实现持续自动化的代码质量改进。受Kaizen（持续改进）理念启发，Pomona自动化了发现与渐进修复的循环：其扫描技能识别改进任务（例如，代码风格违规、技术债务标记及测试缺口），并在结构化的待办事项列表中对其进行优先级排序；同时，修复技能生成面向约10行代码差异的小型拉取请求（PR）。这种人机协同的设计使得频繁、低风险的改进成为可能，同时维护工程师的信任与生产力，并减少技术债务。我们通过在一个团队中部署一个月以及向10名资深工程师发放问卷的方式，对Pomona进行了评估。初步结果令人鼓舞：17个生成的PR中有15个成功合并，中位关闭时间低于2小时。此外，接受调查的工程师中有8/10表达了采用Pomona的意愿，称赞其差异规模小且专注于提升代码质量。最后，我们讨论了供研究人员与从业者参考的、关于在工业中实现有效智能体部署策略的可行见解。