从初级到高级：在代理AI介导的软件工程中分配代理并导航专业成长
From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering
AI总结：
本文通过混合方法研究，探讨了代理AI在软件工程中初级和高级工程师的代理使用情况，发现组织政策而非个人偏好主要限制了代理能力，并提出三种实践以保持编码、学习和指导中的代理能力。
摘要：
初级工程师进入时是AI原生者，高级工程师在中期职业中适应。AI不仅改变工程师编写代码的方式，还在工作和职业成长中重塑谁持有代理。我们通过三阶段混合方法研究，提出了初级和高级工程师在使用代理AI方面的账户：ACTA结合与5名高级工程师的德尔菲过程，10名初级工程师的AI辅助调试任务，以及5名更多高级工程师对初级提示历史的盲审。我们发现，软件工程中的代理能力主要受组织政策限制，而非个人偏好，有经验的开发者通过详细委托保持控制，而新手则在过度依赖和谨慎回避之间挣扎。高级工程师利用AI之前的基础本能来引导现代工具，并拥有有价值的视角，可以指导初级工程师在早期AI鼓励的职业发展中。从结果综合，我们建议三种实践，以保持软件工程在编码、学习和指导中的代理能力，尤其是在AI日益自主的情况下。
英文摘要：
Juniors enter as AI-natives, seniors adapted mid-career. AI is not just changing how engineers code-it is reshaping who holds agency across work and professional growth. We contribute junior-senior accounts on their usage of agentic AI through a three-phase mixed-methods study: ACTA combined with a Delphi process with 5 seniors, an AI-assisted debugging task with 10 juniors, and blind reviews of junior prompt histories by 5 more seniors. We found that agency in software engineering is primarily constrained by organizational policies rather than individual preferences, with experienced developers maintaining control through detailed delegation while novices struggle between over-reliance and cautious avoidance. Seniors leverage pre-AI foundational instincts to steer modern tools and possess valuable perspectives for mentoring juniors in their early AI-encouraged career development. From synthesis of results, we suggest three practices that focus on preserving agency in software engineering for coding, learning, and mentorship, especially as AI grows increasingly autonomous.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
