mlsanityData to 14 Oct 2026

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025

arXiv 2512.14012 ↗First posted 16 Dec 2025cs.SE cs.AI cs.HCcited by 20

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Abstract (arXiv)

The rise of AI agents is transforming how software can be built. The promise of agents is that developers might write code quicker, delegate multiple tasks to different agents, and even write a full piece of software purely out of natural language. In reality, what roles agents play in professional software development remains in question. This paper investigates how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments. Through field observations (N=13) and qualitative surveys (N=99), we find that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes, employing strategies for controlling agent behavior leveraging their expertise. In addition, experienced developers enjoy working with agents as source of collaboration rather than complete delegation given their judgment for task suitability. Our results shed light on the value of software development best practices in effective use of agents, suggest the kinds of tasks for which agents may be suitable, and point towards future opportunities for better agentic interfaces and agentic use guidelines.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 18 of its 57 references that are in the map. First posted on arXiv 16 Dec 2025.

Builds on

18 of 57 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  6. 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 421
  7. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
  8. 2024Human-In-the-Loop Software Development Agentscited by 42
  9. 2023Investigating and Designing for Trust in AI-powered Code Generation Toolscited by 34
  10. 2025Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Reviewcited by 30
  11. 2025Vibe coding: programming through conversation with artificial intelligencecited by 29
  12. 2025Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Codingcited by 11
6 more
  1. 2025Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wildcited by 11
  2. 2025Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Codingcited by 11
  3. 2023A Qualitative Study on the Implementation Design Decisions of Developerscited by 7
  4. 2024Substance Beats Style: Why Beginning Students Fail to Code with LLMscited by 5
  5. 2025Exploring the Challenges and Opportunities of AI-assisted Codebase Generationcited by 4
  6. 2025How Scientists Use Jupyter Notebooks: Goals, Quality Attributes, and Opportunitiescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 20.

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  1. 2026Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agentscited by 6
  2. 2026Codified Context: Infrastructure for AI Agents in a Complex Codebasecited by 5
  3. 2026Building a Correct-by-Design Lakehouse. Data Contracts, Versioning, and Transactional Pipelines for Humans and Agentscited by 4
  4. 2026Coding with "Enemy": Can Human Developers Detect AI Agent Sabotage?cited by 4
  5. 2026Hedwig: Dynamic Autonomy for Coding Agents Under Local Oversightcited by 3
  6. 2026How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Studycited by 2
  7. 2026Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agentscited by 1
  8. 2026The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebasecited by 1
  9. 2026"Skill Issues'': Data-Centric Optimization of Lakehouse Agentscited by 1
  10. 2026Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Studycited by 1
  11. 2026Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Eracited by 0
  12. 2026Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spacescited by 0
8 more
  1. 2026Principal Trait Analysis: Towards Deriving "Skills" in Human-AI Collaborationcited by 0
  2. 2026Engineering Signals of Human-AI Collaboration in the Agentic Coding Era: A Longitudinal Analysis of 33,228 Pull Requests from vLLM and SGLang with Implications for Biomedical AI Agents and Bioinformatics Pipeline Developmencited by 0
  3. 2026The Work Behind Delegation: A Framework for Supervising AI Coding Agentscited by 0
  4. 2026ParallelPilot: Supporting Coordination and Monitoring in Parallel AI Codingcited by 0
  5. 2026A Case Study in Assuring AI-Written Softwarecited by 0
  6. 2026ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Codingcited by 0
  7. 2026Ream: Unfolding Mutual Awareness in Human-Agent Workspacescited by 0
  8. 2026Why Software Engineering Is Indispensable in the Age of Coding Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents
  2. 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
  3. 2025Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows
  4. 2025From Correctness to Collaboration: Toward a Human-Centered Framework for Evaluating AI Agent Behavior in Software Engineering
  5. 2026From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation
  6. 2026Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
  7. 2026Humans Integrate, Agents Fix: How Agent-Authored Pull Requests Are Referenced in Practice
  8. 2026AgentSWE: Can Coding Agents Build the Agent You Actually Want?