# Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code arXiv: https://arxiv.org/abs/2607.09902 ยท HTML: https://mlsanity.com/paper/2607.09902/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 32 of its 89 references that are in the map. - First posted on arXiv 10 Jul 2026. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 30 of 32) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023) - [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021) - [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023) - [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022) - [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025) - [Productivity Assessment of Neural Code Completion](https://mlsanity.com/paper/2205.06537/) (2022) - [Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?](https://mlsanity.com/paper/2204.04741/) (2022) - [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025) - [Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects](https://mlsanity.com/paper/2511.04427/) (2025) - [How much does AI impact development speed? An enterprise-based randomized controlled trial](https://mlsanity.com/paper/2410.12944/) (2024) - [Security and Quality in LLM-Generated Code: A Multi-Language, Multi-Model Analysis](https://mlsanity.com/paper/2502.01853/) (2025) - [Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects](https://mlsanity.com/paper/2406.17910/) (2024) - [Agentic Refactoring: An Empirical Study of AI Coding Agents](https://mlsanity.com/paper/2511.04824/) (2025) - [The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot](https://mlsanity.com/paper/2410.02091/) (2024) - [AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development](https://mlsanity.com/paper/2601.13597/) (2026) - [Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild](https://mlsanity.com/paper/2603.28592/) (2026) - [The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Tasks](https://mlsanity.com/paper/2506.10051/) (2025) - [Investigating The Smells of LLM Generated Code](https://mlsanity.com/paper/2510.03029/) (2025) - [AI-Assisted Programming Decreases the Productivity of Experienced Developers by Increasing the Technical Debt and Maintenance Burden](https://mlsanity.com/paper/2510.10165/) (2025) - [Understanding Dominant Themes in Reviewing Agentic AI-authored Code](https://mlsanity.com/paper/2601.19287/) (2026) - [Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study](https://mlsanity.com/paper/2605.22534/) (2026) - [Do Autonomous Agents Contribute Test Code? A Study of Tests in Agentic Pull Requests](https://mlsanity.com/paper/2601.03556/) (2026) - [Why Agentic-PRs Get Rejected: A Comparative Study of Coding Agents](https://mlsanity.com/paper/2602.04226/) (2026) - [Impact of AI-tooling on the Engineering Workspace](https://mlsanity.com/paper/2406.07683/) (2024) - [Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development](https://mlsanity.com/paper/2409.17434/) (2024) - [Early-Stage Prediction of Review Effort in AI-Generated Pull Requests](https://mlsanity.com/paper/2601.00753/) (2026) - [Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source](https://mlsanity.com/paper/2601.16809/) (2026) - [Beyond Bug Fixes: An Empirical Investigation of Post-Merge Code Quality Issues in Agent-Generated Pull Requests](https://mlsanity.com/paper/2601.20109/) (2026) - [How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses](https://mlsanity.com/paper/2602.17084/) (2026) ## Abstract (arXiv) Agentic coding tools are increasingly used to make autonomous repository-level changes to real-world projects. Prior work has largely evaluated these contributions at the pre-merge stage, through outcomes such as pull request acceptance and review effort. Far less is known about what happens to agentic code post-merge. Yet merge success alone does not reveal whether a contribution will remain stable or require bug fixes and other corrective maintenance downstream. We conduct a longitudinal empirical analysis of agentic and human contributions across 182 repositories, tracking their post-merge fate over time, characterizing the intent of subsequent modifications, and analyzing the defects and vulnerabilities they introduce. While the overall maintenance rates are similar, agentic contributions require significantly higher rates of corrective maintenance and introduce more security weaknesses and dependency vulnerabilities. We also find statistically significant evidence that agentic maintenance burden is associated with repository characteristics. In particular, each 10 percentage-point increase in a project's no-review rate is associated with roughly a 6% increase in agentic maintenance burden on average. As coding agents become pervasive in software development, our findings highlight the need to evaluate and design agentic tools not only to produce mergeable changes, but to produce contributions that remain secure and maintainable.