mlsanityData to 7 Oct 2026

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

arXiv 2602.00496 ↗First posted 31 Jan 2026cs.HC cs.AI cs.SEcited by 1

On the map: LLM agents and tool use → Software engineering coding agents

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.

Key facts. Cited by 1 paper in the map (data through 7 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.

Builds on

29 of 96 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review18 Sep

Other references · 28

  1. 2023Using an LLM to Help With Code Understandingcited by 222
  2. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  3. 2021To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingcited by 118
  4. 2024Need Help? Designing Proactive AI Assistants for Programmingcited by 42
  5. 2025Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistenciescited by 39
  6. 2024Automatic Programming: Large Language Models and Beyondcited by 35
  7. 2023Navigating the Complexity of Generative AI Adoption in Software Engineeringcited by 32
  8. 2023Prompt Problems: A New Programming Exercise for the Generative AI Eracited by 31
  9. 2024The Future of AI-Driven Software Engineeringcited by 23
  10. 2024The Current Challenges of Software Engineering in the Era of Large Language Modelscited by 21
  11. 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 Accomplishmentcited by 19
  12. 2024Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprisecited by 17
16 more
  1. 2023How does HCI Understand Human Autonomy and Agency?cited by 14
  2. 2025Beyond Code Generation: LLM-supported Exploration of the Program Design Spacecited by 13
  3. 2024AI-Tutoring in Software Engineering Educationcited by 11
  4. 2024Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skillscited by 11
  5. 2024LLMs: A Game-Changer for Software Engineers?cited by 11
  6. 2024Demystifying Tacit Knowledge in Graphic Design: Characteristics, Instances, Approaches, and Guidelinescited by 10
  7. 2023Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomescited by 9
  8. 2023How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineeringcited by 9
  9. 2025Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Makingcited by 9
  10. 2021Automatic Detection of Five API Documentation Smells: Practitioners' Perspectivescited by 6
  11. 2023Human AI Collaboration in Software Engineering: Lessons Learned from a Hands On Workshopcited by 6
  12. 2025Knowledge Workers' Perspectives on AI Training for Responsible AI Usecited by 4
  13. 2018Knowledge Management in Software Engineering: A Systematic Review of Studied Concepts, Findings and Research Methods Usedcited by 3
  14. 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Reviewcited by 3
  15. 2020Characterizing the Roles of Contributors in Open-source Scientific Software Projectscited by 2
  16. 2024Generative AI in the Software Engineering Domain: Tensions of Occupational Identity and Patterns of Identity Protectioncited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026"If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplacecited by 2

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