Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles
- URL: http://arxiv.org/abs/2605.08017v2
- Date: Wed, 13 May 2026 02:55:29 GMT
- Title: Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles
- Abstract summary: We analyze 29,585 PR lifecycles using an Initiator xr taxonomy with six interaction scenarios.<n>We characterize tools along a Collaborator-Assistant spectrum in how they redistribute initiative, oversight, and endorsement.<n>We contribute the taxonomy, per-tool state machines, and a replication package for research on automation, oversight, and governance in PR.
- Score: 0.8777271346549705
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: When AI coding agents open branches and submit pull requests (PRs), two questions co-determine oversight design: who starts the work (operational agency) and who authorizes its completion (merge governance). We characterize tools along a Collaborator-Assistant spectrum in how they redistribute initiative, oversight, and endorsement, while merge governance remains predominantly human across five tools (OpenAI, Copilot, Devin, Cursor, Claude Code). We analyze 29,585 PR lifecycles using an Initiator x Approver taxonomy with six interaction scenarios; lifecycle reconstruction supplies the how behind those roles. Collaborator tools (Cursor, Devin, Copilot) concentrate operational initiative in agents that open and carry PR work forward, with humans retaining review and endorsement on the path to merge; Assistant tools (OpenAI, Claude) leave task direction primarily with humans and supply bounded support within human-led workflows. Across the spectrum, agency and governance decouple: Collaborator workflows are >=96% agent initiated, yet terminal merge authority remains almost exclusively human, with agent-classified approvers confined to a small fraction of PRs. Where automation executes a merge, logs record the executor but not the decision-maker, marking a boundary of observation. We contribute the taxonomy, per-tool state machines, and a replication package for research on automation, oversight, and governance in PR workflows.
Related papers
- Dr. Claw: An AI Scientist Workspace for Vibe Research [64.18459471289147]
Dr. Claw is an open-source workspace that wraps existing coding-agent executors in a controllable human-in-the-loop workflow.<n> Persistent state objects, a reusable skill library, and multi-executor coordination link human decisions to AI execution.<n>Dr. Claw scores higher on research completeness while persisting an auditable, recoverable process trail.
arXiv Detail & Related papers (2026-08-31T20:58:47Z) - Code as Agent Harness [107.31925305395957]
In emerging agentic systems, code is no longer only a target output.<n>Code increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification.<n>This survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.
arXiv Detail & Related papers (2026-05-18T17:59:03Z) - Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems [65.90791804095561]
Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination amplifies a less explored risk.<n>Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined.<n>This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement.
arXiv Detail & Related papers (2026-05-14T14:36:13Z) - Workstream: A Local-First Developer Command Center for the AI-Augmented Engineering Workflow [0.0]
We present Workstream, an open-source, local-first developer command center that aggregates pull requests, task management, calendar, AI-powered code review, historical review intelligence, repository AI-readiness scoring, and agent observability into a single interface.<n>We describe the system architecture, a novel 5-category AI readiness scoring algorithm, a review intelligence pipeline that mines historical PR reviews for team-specific patterns, and an agent observability layer implementing the Model Context Protocol (MCP), Agent-to-Agent (A2A), and Agent Observability Protocol (AOP)
arXiv Detail & Related papers (2026-04-18T16:24:29Z) - Humans Integrate, Agents Fix: How Agent-Authored Pull Requests Are Referenced in Practice [4.359007050137919]
We introduce a taxonomy to characterize the intent of agent-authored PR references across Human-to-Agent and Agent-to-Agent interactions.<n>Our analysis shows that while humans initiate most references to agent-authored PRs, a substantial portion of these interactions are AI-assisted.<n>We find that referencing/referenced PRs are associated with substantially longer lifespan and review times compared to isolated PRs.
arXiv Detail & Related papers (2026-04-05T11:04:46Z) - AI Co-Scientist for Ranking: Discovering Novel Search Ranking Models alongside LLM-based AI Agents with Cloud Computing Access [60.37646376282544]
We present an AI Co-Scientist framework that automates the full search ranking research pipeline.<n>This is the first study in the ranking community to utilize an AI Co-Scientist framework for algorithmic research.
arXiv Detail & Related papers (2026-03-23T09:29:08Z) - Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study [5.127121704630949]
We analyze 8,106 fix related PRs authored by five widely used AI coding agents from the AIDEV POP dataset.<n>Our results indicate that test case failures and prior resolution of the same issues by other PRs are the most common causes of non integration.
arXiv Detail & Related papers (2026-01-29T22:06:58Z) - On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source [11.412808537439973]
We investigated project-level guidelines and developers' interactions with AI-assisted pull requests (PRs)<n>We found that over 67.5% of AI-co-authored PRs originate from contributors without prior code ownership.<n>In contrast to human-created PRs where non-owner developers receive the most feedback, AI-co-authored PRs from non-owners receive the least.
arXiv Detail & Related papers (2026-01-20T09:09:53Z) - Early-Stage Prediction of Review Effort in AI-Generated Pull Requests [0.0]
We analyze 33,707 agent-authored PRs from the AIDev dataset across 2,807 repositories.<n>We propose a Circuit Breaker triage model that predicts high-review-effort PRs at creation time.
arXiv Detail & Related papers (2026-01-02T17:18:01Z) - Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows [60.04362496037186]
We present the first controlled study of developer interactions with coding agents.<n>We evaluate two leading copilot and agentic coding assistants.<n>Our results show agents can assist developers in ways that surpass copilots.
arXiv Detail & Related papers (2025-07-10T20:12:54Z) - WHALES: A Multi-Agent Scheduling Dataset for Enhanced Cooperation in Autonomous Driving [14.977628973132099]
We introduce WHALES, the first large-scale V2X dataset explicitly designed to benchmark communication-aware agent scheduling and scalable cooperative perception.<n>WHALES features an average of 8.4 cooperative agents per scene and 2.01 million annotated 3D objects across diverse traffic scenarios.<n>It incorporates detailed communication metadata to emulate real-world communication bottlenecks, enabling rigorous evaluation of scheduling strategies.
arXiv Detail & Related papers (2024-11-20T14:12:34Z) - ProAgent: From Robotic Process Automation to Agentic Process Automation [87.0555252338361]
Large Language Models (LLMs) have emerged human-like intelligence.
This paper introduces Agentic Process Automation (APA), a groundbreaking automation paradigm using LLM-based agents for advanced automation.
We then instantiate ProAgent, an agent designed to craft from human instructions and make intricate decisions by coordinating specialized agents.
arXiv Detail & Related papers (2023-11-02T14:32:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.