# Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wild

arXiv: https://arxiv.org/abs/2506.12347 · HTML: https://mlsanity.com/paper/2506.12347/

## Key facts

- Cited by 11 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 14 Jun 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, 11 of 11)

- [Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025](https://mlsanity.com/paper/2512.14012/) (2025)
- [On the Use of Agentic Coding Manifests: An Empirical Study of Claude Code](https://mlsanity.com/paper/2509.14744/) (2025)
- [Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity](https://mlsanity.com/paper/2601.18345/) (2026)
- [Are Coding Agents Generating Over-Mocked Tests? An Empirical Study](https://mlsanity.com/paper/2602.00409/) (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)
- [Humans are Missing from AI Coding Agent Research](https://mlsanity.com/paper/2608.12355/) (2026)
- [Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI](https://mlsanity.com/paper/2607.01418/) (2026)
- [Beyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle](https://mlsanity.com/paper/2609.04681/) (2026)
- [Where Is the Tradeoff in Using Third-Party API Routers for Agentic Software Development?](https://mlsanity.com/paper/2607.23624/) (2026)
- [Plans Work in Mysterious Ways: Evaluating a Plan Mode for Spreadsheet Agents](https://mlsanity.com/paper/2607.23670/) (2026)
- [Introducing Consort: A Spec-First Agent Framework for Enforced, Test-Driven Development on Live Database Branches](https://mlsanity.com/paper/2609.09671/) (2026)

## Abstract (arXiv)

Software Engineering Agents (SWE agents) can autonomously perform development tasks on benchmarks like SWE Bench, but still face challenges when tackling complex and ambiguous real-world tasks. Consequently, SWE agents are often designed to allow interactivity with developers, enabling collaborative problem-solving. To understand how developers collaborate with SWE agents and the barriers they face in such interactions, we observed 19 developers using an in-IDE agent to resolve 33 open issues in repositories to which they had previously contributed. Participants successfully resolved about half of these issues, with those solving issues incrementally having greater success than those using a one-shot approach. Participants who actively collaborated with the agent and iterated on its outputs were also more successful, though they faced challenges in trusting the agent's responses and collaborating on debugging and testing. Our findings suggest that to facilitate successful collaborations, both SWE agents and developers should actively contribute to tasks throughout all stages of the software development process. SWE agents can enable this by challenging and engaging in discussions with developers, rather than being conclusive or sycophantic.