From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering

TL;DR

This study explores how AI impacts agency and professional growth in software engineering, highlighting differences between junior and senior developers and proposing practices to preserve agency amid increasing AI autonomy.

Contribution

It provides empirical insights into agency dynamics in AI-assisted software engineering and offers practical recommendations for maintaining agency during AI integration.

Findings

Organizational policies constrain developer agency more than individual preferences.

Seniors use foundational instincts to guide AI tools and mentor juniors.

Three practices are proposed to preserve agency in AI-augmented coding, learning, and mentorship.

Abstract

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.