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.

Agentic AI systems powered by Large Language Models (LLMs) are increasingly used to autonomously contribute code in modern software development. While prior work has shown that such systems can accelerate development tasks, their reliability and maintenance behavior in real-world Continuous Integration (CI) workflows remain poorly understood. In this study, we analyze 11,771 pull requests (PRs) from GitHub, including 7,619 agentic and 4,152 human-authored PRs, to investigate how agentic code behaves during CI workflows. We examine (1) CI failure rates at the pull-request level, (2) responsibility for introducing and fixing CI failures, and (3) time-to-fix at the commit level using fail–fix mappings. Our results show that human-authored CI fixes exhibit a median time to fix of 71.70 minutes, whereas AI agentic-authored CI fixes resolve failures nearly four times faster, with a median of 17.23 minutes. Our results show that agent-authored fixes resolve CI failures nearly four times faster than human fixes (median 17.23 vs. 71.70 minutes). However, agents introduce most CI failures (79.15%) while performing a smaller share of fixes (60.63%), indicating that human developers remain heavily involved in failure resolution despite faster agent responses.