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

2026-01-31

AI Summary

This study investigates how artificial intelligence is reshaping the professional roles and agency distribution of software engineers, revealing distinct challenges faced by junior and senior developers in AI collaboration. Employing a mixed-methods approach—including Applied Cognitive Task Analysis (ACTA), Delphi studies, AI debugging experiments, and blinded prompt logs—the research systematically compares work practices, learning behaviors, and agency enactment between the two groups. Findings indicate that organizational policies constrain agency far more significantly than individual preferences; senior engineers maintain control through fine-grained delegation, whereas novices often oscillate between over-reliance on and avoidance of AI tools. Building on these insights, the work proposes a human-centered AI collaboration framework integrating coding, learning, and mentoring dimensions, and offers three actionable recommendations to safeguard human primacy in AI-augmented software development.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• agentic AI
• software engineering
• professional agency
• career development
• human-AI collaboration

Innovation

• agentic AI
• professional agency
• software engineering
• AI-mediated collaboration
• career development