AI RESEARCH
XAI for Coding Agent Failures: Transforming Raw Execution Traces into Actionable Insights
arXiv CS.AI
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ArXi:2603.05941v1 Announce Type: cross Large Language Model (LLM)-based coding agents show promise in automating software development tasks, yet they frequently fail in ways that are difficult for developers to understand and debug. While general-purpose LLMs like GPT can provide ad-hoc explanations of failures, raw execution traces remain challenging to interpret even for experienced developers. We present a systematic explainable AI (XAI) approach that transforms raw agent execution traces into structured, human-interpretable explanations.