# Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows

arXiv: https://arxiv.org/abs/2507.08149 · HTML: https://mlsanity.com/paper/2507.08149/

## Key facts

- Cited by 28 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.
- First posted on arXiv 10 Jul 2025.

## Placed in

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

## Built on by (most cited first, 28 of 28)

- [Mind the Sim2Real Gap in User Simulation for Agentic Tasks](https://mlsanity.com/paper/2603.11245/) (2026)
- [Training Proactive and Personalized LLM Agents](https://mlsanity.com/paper/2511.02208/) (2025)
- [How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations](https://mlsanity.com/paper/2510.22780/) (2025)
- [How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions](https://mlsanity.com/paper/2605.29442/) (2026)
- [AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development](https://mlsanity.com/paper/2601.13597/) (2026)
- [Completion neq Collaboration: Scaling Collaborative Effort with Agents](https://mlsanity.com/paper/2510.25744/) (2025)
- [Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding](https://mlsanity.com/paper/2602.00854/) (2026)
- [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)
- [How can we assess human-agent interactions? Case studies in software agent design](https://mlsanity.com/paper/2510.09801/) (2025)
- [Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents](https://mlsanity.com/paper/2601.20106/) (2026)
- [Humans are Missing from AI Coding Agent Research](https://mlsanity.com/paper/2608.12355/) (2026)
- [Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Model](https://mlsanity.com/paper/2608.13867/) (2026)
- [Reflections and New Directions for Human-Centered Large Language Models](https://mlsanity.com/paper/2605.06901/) (2026)
- [On the Adoption of AI Coding Agents in Open-source Android and iOS Development](https://mlsanity.com/paper/2602.12144/) (2026)
- [Comparing Developer and LLM Biases in Code Evaluation](https://mlsanity.com/paper/2603.24586/) (2026)
- [The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase](https://mlsanity.com/paper/2603.25697/) (2026)
- [RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions](https://mlsanity.com/paper/2605.01104/) (2026)
- [Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code](https://mlsanity.com/paper/2605.13280/) (2026)
- [Offloading Score: Measuring AI Reliance Through Counterfactual Workflows](https://mlsanity.com/paper/2605.29392/) (2026)
- [RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies](https://mlsanity.com/paper/2603.11001/) (2026)
- [Human agency in initial human-AI proof formalization workflows](https://mlsanity.com/paper/2606.04273/) (2026)
- [From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software](https://mlsanity.com/paper/2607.05677/) (2026)
- [Early Adoption of Agentic Coding Tools by GitHub Projects](https://mlsanity.com/paper/2607.14037/) (2026)
- [MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems](https://mlsanity.com/paper/2608.16181/) (2026)
- [Generation of Web Apps with Agentic IDEs: An Empirical Assessment](https://mlsanity.com/paper/2608.20903/) (2026)
- [VibeJam: A User Study Platform for Web Development with Agents](https://mlsanity.com/paper/2608.29889/) (2026)
- [Dynamic Adaptation of the LLM Context for Generating Routines with Coupled Semantics](https://mlsanity.com/paper/2609.04570/) (2026)
- [Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows](https://mlsanity.com/paper/2609.26725/) (2026)

## Abstract (arXiv)

Developers now have access to a growing array of increasingly autonomous AI tools for software development. While many studies examine copilots that provide chat assistance or code completions, evaluations of coding agents -- which can automatically write files and run code -- still rely on static benchmarks. We present the first controlled study of developer interactions with coding agents, characterizing how more autonomous AI tools affect productivity and experience. We evaluate two leading copilot and agentic coding assistants, recruiting participants who regularly use the former. Our results show agents can assist developers in ways that surpass copilots (e.g., completing tasks humans may not have accomplished) and reduce the effort required to finish tasks. Yet challenges remain for broader adoption, including ensuring users adequately understand agent behaviors. Our findings reveal how workflows shift with coding agents and how interactions differ from copilots, motivating recommendations for researchers and highlighting challenges in adopting agentic systems.