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

arXiv: https://arxiv.org/abs/2602.00496 · HTML: https://mlsanity.com/paper/2602.00496/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 29 of its 96 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 29 of 29)

- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [Need Help? Designing Proactive AI Assistants for Programming](https://mlsanity.com/paper/2410.04596/) (2024)
- [Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies](https://mlsanity.com/paper/2502.08554/) (2025)
- [Automatic Programming: Large Language Models and Beyond](https://mlsanity.com/paper/2405.02213/) (2024)
- [Navigating the Complexity of Generative AI Adoption in Software Engineering](https://mlsanity.com/paper/2307.06081/) (2023)
- [Prompt Problems: A New Programming Exercise for the Generative AI Era](https://mlsanity.com/paper/2311.05943/) (2023)
- [The Future of AI-Driven Software Engineering](https://mlsanity.com/paper/2406.07737/) (2024)
- [The Current Challenges of Software Engineering in the Era of Large Language Models](https://mlsanity.com/paper/2412.14554/) (2024)
- ["If the Machine Is As Good As Me, Then What Use Am I?" -- How the Use of ChatGPT Changes Young Professionals' Perception of Productivity and Accomplishment](https://mlsanity.com/paper/2404.12549/) (2024)
- [Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise](https://mlsanity.com/paper/2412.06603/) (2024)
- [How does HCI Understand Human Autonomy and Agency?](https://mlsanity.com/paper/2301.12490/) (2023)
- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- [Beyond Code Generation: LLM-supported Exploration of the Program Design Space](https://mlsanity.com/paper/2503.06911/) (2025)
- [AI-Tutoring in Software Engineering Education](https://mlsanity.com/paper/2404.02548/) (2024)
- [LLMs: A Game-Changer for Software Engineers?](https://mlsanity.com/paper/2411.00932/) (2024)
- [Demystifying Tacit Knowledge in Graphic Design: Characteristics, Instances, Approaches, and Guidelines](https://mlsanity.com/paper/2403.06252/) (2024)
- [Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes](https://mlsanity.com/paper/2311.18452/) (2023)
- [How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineering](https://mlsanity.com/paper/2312.11719/) (2023)
- [Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Making](https://mlsanity.com/paper/2503.18419/) (2025)
- [Human AI Collaboration in Software Engineering: Lessons Learned from a Hands On Workshop](https://mlsanity.com/paper/2312.10620/) (2023)
- [Automatic Detection of Five API Documentation Smells: Practitioners' Perspectives](https://mlsanity.com/paper/2102.08486/) (2021)
- [Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review](https://mlsanity.com/paper/2409.11864/) (2024)
- [Knowledge Workers' Perspectives on AI Training for Responsible AI Use](https://mlsanity.com/paper/2503.06002/) (2025)
- [Knowledge Management in Software Engineering: A Systematic Review of Studied Concepts, Findings and Research Methods Used](https://mlsanity.com/paper/1811.12278/) (2018)
- [Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review](https://mlsanity.com/paper/2503.07556/) (2025)
- [Characterizing the Roles of Contributors in Open-source Scientific Software Projects](https://mlsanity.com/paper/2003.10572/) (2020)
- [Generative AI in the Software Engineering Domain: Tensions of Occupational Identity and Patterns of Identity Protection](https://mlsanity.com/paper/2410.03571/) (2024)

## Built on by (most cited first, 1 of 1)

- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)

## Abstract (arXiv)

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.