# How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses

arXiv: https://arxiv.org/abs/2602.17084 · HTML: https://mlsanity.com/paper/2602.17084/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 19 Feb 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, 11 of 11)

- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming](https://mlsanity.com/paper/2210.14306/) (2022)
- [Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap](https://mlsanity.com/paper/2410.06107/) (2024)
- [AutoDev: Automated AI-Driven Development](https://mlsanity.com/paper/2403.08299/) (2024)
- [Experience with GitHub Copilot for Developer Productivity at Zoominfo](https://mlsanity.com/paper/2501.13282/) (2025)
- [Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?](https://mlsanity.com/paper/2004.00300/) (2020)
- [Developers' Perceptions on the Impact of ChatGPT in Software Development: A Survey](https://mlsanity.com/paper/2405.12195/) (2024)
- [The Landscape of Toxicity: An Empirical Investigation of Toxicity on GitHub](https://mlsanity.com/paper/2502.08238/) (2025)
- [Are We on the Same Page? Examining Developer Perception Alignment in Open Source Code Reviews](https://mlsanity.com/paper/2504.18407/) (2025)

## Built on by (most cited first, 3 of 3)

- [Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code](https://mlsanity.com/paper/2607.09902/) (2026)
- [AgenTag: Attribution of AI Coding Agents from Behavioral Fingerprints](https://mlsanity.com/paper/2608.00966/) (2026)
- [Who Finishes the Job? A Study of Follow-Up Fixes and Commit Authorship on AI Coding Agent Pull Requests](https://mlsanity.com/paper/2609.26847/) (2026)

## Abstract (arXiv)

The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request description characteristics, and how human reviewers respond to them, remains underexplored. In this study, we conduct an empirical analysis of pull requests created by five AI coding agents using the AIDev dataset. We analyze agent differences in pull request description characteristics, including structural features, and examine human reviewer response in terms of review activity, response timing, sentiment, and merge outcomes. We find that AI coding agents exhibit distinct PR description styles, which are associated with differences in reviewer engagement, response time, and merge outcomes. We observe notable variation across agents in both reviewer interaction metrics and merge rates. These findings highlight the role of pull request presentation and reviewer interaction dynamics in human-AI collaborative software development.