How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
arXiv:2605.29442v1 Announce Type: cross Abstract: AI coding agents increasingly act directly within software environments, yet existing analyses of their failures rely on benchmark trajectories that miss how developers actually experience misalignment. We present an observational study of 20,574 coding-agent sessions from 1,639 repositories across IDE and CLI workflows. We operationalize misalignment as a breakdown made visible through developer pushback, and annotate each episode along four axes: form, cause, cost, and resolution. We identify seven recurring forms, spanning how agents read pr
This study emerges as AI coding agents are rapidly being integrated into developer workflows, making actual user experience and failure modes critical for their next stage of development.
Understanding how coding agents fail their users in real-world scenarios is crucial for improving their design, increasing developer adoption, and accelerating the autonomous agent paradigm.
The focus for AI coding agent development shifts from pure benchmark performance to deeply understanding and mitigating 'misalignment' as experienced by developers in everyday tasks.
- · Companies developing robust AI agent feedback loops
- · Developers leveraging advanced coding agents
- · AI agent-assisted software development sector
- · AI agent developers ignoring user misalignment
- · Single-metric AI agent benchmarks
- · Traditional software development methods
Improved reliability and functionality of AI coding agents through better understanding of failure modes.
Increased developer productivity and adoption of AI assistants, leading to faster software development cycles.
The acceleration of fully autonomous agentic systems that can independently complete complex software engineering tasks.
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Read at arXiv cs.AI