How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions

2026-05-28

AI Summary

This study addresses a critical gap in current AI coding agent research, which predominantly relies on idealized benchmarks and overlooks misalignment between user intent and agent behavior in real-world development. Leveraging 20,574 real-world IDE and CLI sessions spanning 1,639 repositories, the authors conduct large-scale log mining and manual annotation to formally define misalignment as interaction breakdowns revealed by developer interventions. They systematically categorize misalignment along four dimensions: form, cause, cost, and resolution strategy, identifying seven prevalent patterns. The analysis reveals cross-environment variations, session persistence, and temporal evolution trends. Notably, 90.50% of misalignments primarily impose cognitive load and erode trust, while 91.49% require explicit user correction. Furthermore, issues such as constraint violations and self-report inaccuracies exhibit significant increases over time.

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 projects, interpret developer intent, follow rules, bound their actions, implement and execute code, and report progress. 90.50\% of episodes impose effort and trust costs rather than irreversible system damage, yet 91.49\% of visible resolutions still require explicit user correction. Misalignment patterns also differ across IDE and CLI settings, persist across adjacent sessions, and shift over time: while overall rates decline, constraint violations and inaccurate self-reporting grow in share. Our findings inform the design of training, evaluation, and interfaces for keeping coding agents aligned with real developer workflows.

Problem

• coding agents
• developer-agent misalignment
• real-world sessions
• AI failure analysis
• software development workflows

Innovation

• developer-agent misalignment
• observational study
• coding agents
• human-AI interaction
• real-world sessions