Characterizing Self-Admitted Technical Debt Generated by AI Coding Agents

Large Language Models (LLMs) are increasingly used through autonomous agents (e.g., Copilot, Cursor, Devin, Claude) to perform complex software development tasks. However, little is known about how these agents introduce and document technical debt through Self-Admitted Technical Debt (SATD) comments. Understanding SATD in AI-generated code is critical, as such comments explicitly reveal acknowledged limitations and deferred fixes that affect long-term maintenance. In this study, we quantitatively and qualitatively analyze 525 SATD comments authored by AI agents using the AIDev dataset. Our results show that AI-generated SATD is slightly more technically detailed than human-authored SATD, yet both often describe problems without clear guidance on resolution. Through thematic analysis, we identify 34 SATD topics grouped into 10 categories, with AI agents predominantly documenting requirement- and design-related debt. While many SATD topics overlap between AI and humans, our taxonomy reveals new debt categories and emphases specific to AI-authored SATD, particularly related to infrastructure, pipelines, dependency management, and requirement interpretation driven by developer prompts. Overall, our findings suggest that AI- and human-authored SATD share common characteristics but differ in expression and focus, highlighting the need for deeper investigation into how agentic systems communicate and manage technical debt.