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

arXiv: https://arxiv.org/abs/2512.14012 · HTML: https://mlsanity.com/paper/2512.14012/

## 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 (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Human-In-the-Loop Software Development Agents](https://mlsanity.com/paper/2411.12924/) (2024)
- [Investigating and Designing for Trust in AI-powered Code Generation Tools](https://mlsanity.com/paper/2305.11248/) (2023)
- [Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Review](https://mlsanity.com/paper/2510.00328/) (2025)
- [Vibe coding: programming through conversation with artificial intelligence](https://mlsanity.com/paper/2506.23253/) (2025)
- [Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Coding](https://mlsanity.com/paper/2503.16508/) (2025)
- [Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wild](https://mlsanity.com/paper/2506.12347/) (2025)
- [Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding](https://mlsanity.com/paper/2509.12491/) (2025)
- [A Qualitative Study on the Implementation Design Decisions of Developers](https://mlsanity.com/paper/2301.09789/) (2023)
- [Substance Beats Style: Why Beginning Students Fail to Code with LLMs](https://mlsanity.com/paper/2410.19792/) (2024)
- [Exploring the Challenges and Opportunities of AI-assisted Codebase Generation](https://mlsanity.com/paper/2508.07966/) (2025)
- [How Scientists Use Jupyter Notebooks: Goals, Quality Attributes, and Opportunities](https://mlsanity.com/paper/2503.12309/) (2025)

## Built on by (most cited first, 20 of 20)

- [Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents](https://mlsanity.com/paper/2606.05391/) (2026)
- [Codified Context: Infrastructure for AI Agents in a Complex Codebase](https://mlsanity.com/paper/2602.20478/) (2026)
- [Building a Correct-by-Design Lakehouse. Data Contracts, Versioning, and Transactional Pipelines for Humans and Agents](https://mlsanity.com/paper/2602.02335/) (2026)
- [Coding with "Enemy": Can Human Developers Detect AI Agent Sabotage?](https://mlsanity.com/paper/2606.05647/) (2026)
- [Hedwig: Dynamic Autonomy for Coding Agents Under Local Oversight](https://mlsanity.com/paper/2605.11495/) (2026)
- [How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study](https://mlsanity.com/paper/2607.10856/) (2026)
- [Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agents](https://mlsanity.com/paper/2602.19065/) (2026)
- [The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase](https://mlsanity.com/paper/2603.25697/) (2026)
- ["Skill Issues'': Data-Centric Optimization of Lakehouse Agents](https://mlsanity.com/paper/2606.01185/) (2026)
- [Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study](https://mlsanity.com/paper/2606.12231/) (2026)
- [Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era](https://mlsanity.com/paper/2601.11739/) (2026)
- [Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces](https://mlsanity.com/paper/2607.05127/) (2026)
- [Principal Trait Analysis: Towards Deriving "Skills" in Human-AI Collaboration](https://mlsanity.com/paper/2608.11460/) (2026)
- [Engineering 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 Developmen](https://mlsanity.com/paper/2608.13884/) (2026)
- [The Work Behind Delegation: A Framework for Supervising AI Coding Agents](https://mlsanity.com/paper/2609.24234/) (2026)
- [ParallelPilot: Supporting Coordination and Monitoring in Parallel AI Coding](https://mlsanity.com/paper/2609.33113/) (2026)
- [A Case Study in Assuring AI-Written Software](https://mlsanity.com/paper/2610.08651/) (2026)
- [ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding](https://mlsanity.com/paper/2610.08662/) (2026)
- [Ream: Unfolding Mutual Awareness in Human-Agent Workspaces](https://mlsanity.com/paper/2610.09497/) (2026)
- [Why Software Engineering Is Indispensable in the Age of Coding Agents](https://mlsanity.com/paper/2610.10226/) (2026)

## 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.