Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 30 references that are in the map. First posted on arXiv 8 May 2026.
Builds on
8 of 30 references.
Closest ancestors · 5
Same direction, newest first
- 2026AIDev: Studying AI Coding Agents on GitHub9 Feb
- 2026Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance9 Feb
- 2026A Task-Level Evaluation of AI Agents in Open-Source Projects2 Feb
- 2026AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development20 Jan
- 2025Agentic AI Software Engineers: Programming with Trust19 Feb
Other references · 3
- 2025How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineeringcited by 14
- 2026On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Sourcecited by 4
- 2025Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Studycited by 3
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Who Finishes the Job? A Study of Follow-Up Fixes and Commit Authorship on AI Coding Agent Pull Requests
- 2026Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study
- 2026Humans Integrate, Agents Fix: How Agent-Authored Pull Requests Are Referenced in Practice
- 2026Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub
- 2026How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests
- 2026AI Agent Pull Requests on GitHub: Frequency, Structure, and Merge Conflict Rates
- 2026Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests
- 2026Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study