mlsanityData to 7 Oct 2026

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

arXiv 2506.12347 ↗First posted 14 Jun 2025cs.SE cs.HCcited by 11

On the map: LLM agents and tool use → Software engineering coding agents

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.

Key facts. Cited by 11 papers in the map (data through 7 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.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 9.

11 papers.

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. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
  3. 2026How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study
  4. 2025The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
  5. 2025Saving SWE-Bench: A Benchmark Mutation Approach for Realistic Agent Evaluation
  6. 2026SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?
  7. 2025SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling
  8. 2026SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions