The paper titled "Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025" examines how experienced developers employ AI agents in software development. Through field observations and surveys, it finds that developers value agents for productivity but maintain control over design and implementation to ensure quality. They use agents as collaborative tools rather than full delegation, selecting tasks based on suitability and leveraging their expertise to guide agent behavior.
arXiv:2607. 21832v1 Announce Type: cross Abstract: Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows.
arXiv:2606. 21894v2 Announce Type: replace-cross Abstract: As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers?
arXiv:2607. 14037v1 Announce Type: cross Abstract: Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development.
arXiv:2608. 04148v1 Announce Type: cross Abstract: Agentic AI is increasingly used to coordinate planning, implementation, review, and testing in software development, yet it often offers limited transparency into its decisions and interactions.
arXiv:2607. 10856v1 Announce Type: cross Abstract: The rise of Software Engineering (SE) agents, i.
Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding tools are adopted and managed at the project level.
arXiv:2608. 12355v1 Announce Type: cross Abstract: Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows.
arXiv:2606. 05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
arXiv:2606. 05391v1 Announce Type: cross Abstract: Autonomous software agents hold promise to increase developer productivity but make mistakes and exhibit novel failure modes, making human oversight central to successful human-agent collaboration.
arXiv:2510. 09801v3 Announce Type: replace Abstract: While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases.
arXiv:2510. 04452v3 Announce Type: replace-cross Abstract: Computer use agents (or "agents") are generative AI that automates actions within user interfaces from user commands.
arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.