Agent pull requests are proposed repository changes authored or substantially produced by coding agents and submitted through the usual pull request (PR) workflow. They can become a review bottleneck when the volume of proposed changes—or the work needed to understand and validate each one—outstrips available reviewer attention. The evidence identifies concrete sources of effort, including broad diffs, failing CI, poor project fit and unclear intent; it does not establish that agent adoption universally increases review queues or delays.
What is an agent pull request?
An agent PR is a proposed change to a code repository that a coding agent authored or substantially produced. It is still a proposal, not a completed integration: people and automated checks must assess whether it solves the right problem, fits the project, passes relevant tests and should be merged.
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The workflow itself is familiar. A PR presents a diff for discussion and review; reviewers may request changes, approve it, or leave it unmerged. The agent’s speed at producing a change does not remove those decisions or the need for a responsible owner.
Why can agent PRs create review bottlenecks?
A bottleneck forms when proposed work arrives faster than reviewers can judge it, or when each proposal takes substantial effort to understand. Review is more than reading code: it can require checking the original task against the implementation, learning why the change was made, comparing it with local conventions and architecture, assessing tests and CI, and deciding whether the work belongs in the project.
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Broad diffs raise the cost of understanding
In a study of 33,000 agent-authored PRs across five coding agents, PRs that were not merged tended to contain more changed lines and touch more files than merged PRs. This is an association with non-merge outcomes, not proof that size alone caused rejection. Still, a change spanning many files can demand more reviewer context and make it harder to isolate whether the implementation matches the task. The study, “Where Do AI Coding Agents Fail?”, also reports that documentation, CI and build-update tasks had the highest merge success in its sampled projects, while performance and bug-fix tasks performed worst.
Failing checks add validation work
The same failed-PR study found that unmerged agent PRs failed CI validation more often. A failure does not automatically mean the code is wrong, and the study does not establish CI failure as the sole cause of rejection. But reviewers may need to determine whether a failure is new, relevant, reproducible or unrelated before they can make a merge decision.
Correct code can still be the wrong change
Reviewers must judge project fit and task intent, not just whether code runs. In a qualitative examination of 600 rejected agentic PRs, the authors identified duplicate submissions, unwanted feature implementations, agent misalignment and a lack of meaningful reviewer engagement. In such cases, the review problem may be deciding whether the change should exist at all—not finding a defect in its implementation. These findings describe cases in the sampled GitHub projects, not a claim that every agent PR has these problems.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsUnclear rationale shifts work to the reviewer
If a description does not explain the task, assumptions or trade-offs, reviewers may have to reconstruct the reasoning from the diff. That makes a PR harder to evaluate even when its scope is reasonable. A September 2025 empirical study of agentic coding recommends dividing broad work into small, self-contained PRs, making project-specific rules available to agents and adding review scaffolding that explains rationale, assumptions, alternatives and edge cases. Read the study, “On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub.”
What do studies say about human review effort?
The evidence does not reduce to “agent PRs always get more review” or “agent PRs are faster.” Different studies measure different outcomes, and their results point to a distinction between how often intervention happens and how much work it takes when it does.
Khelifi, Ouni and Khemaja report that human intervention occurred in 52.17% of agent-authored PRs, compared with 83.59% of human-authored PRs. However, intervention in agent PRs required greater effort, including larger code churn and longer durations. The authors classify intervention as guidance-level (58.02%), decision-level (21.16%), direct code changes (17.05%) and operational-level (3.69%). These figures describe the study’s categories and sample; intervention frequency is not the same measure as review duration or organization-wide queue latency. See the MSR 2026 proceedings record for “Behind Agentic Pull Requests.”
A separate study compared 24,014 merged agentic PRs with 5,081 merged human PRs and reported differences in commit counts, as well as moderate differences in files touched and deleted lines. Because its analysis covers merged contributions, it cannot by itself establish rejection rates or backlog effects. The study is “How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests.”
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How teams can make agent PRs easier to review
These practices aim to make proposals clearer and more bounded. The cited work recommends or identifies them as useful review considerations; it does not establish that any one practice guarantees shorter review times.
Break broad work into reviewable units
Give an agent a task that can be handled in one focused PR. For a larger change, define a sequence of self-contained PRs so a reviewer can assess each decision without having to absorb the whole project at once.
Provide project-specific expectations
Make relevant formatting rules, design principles, architectural constraints, and testing and documentation expectations available in the agent’s instructions. The 2025 study identifies style mismatch, unnecessary refactoring, missing documentation and missing tests among reasons for revisions.
Ask for an explanation alongside the diff
Have the agent describe the task it addressed, its implementation plan, key assumptions, alternatives considered, known edge cases, tests run and limitations. This gives reviewers a way to compare the stated intent with the actual change instead of inferring all of the rationale from code.
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Keep task alignment and CI results visible
Include a link or clear reference to the original task and summarize relevant checks, including failures. Reviewers can then compare the request with the delivered behavior and see what still needs investigation.
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Keep a human accountable for the merge decision
Automation can surface issues or handle routine checks, but a person should remain responsible for deciding whether a change belongs in the project and is ready to ship. A study of code-review bots across 1,194 GitHub open-source projects found effects that varied by outcome and project setting; it does not support treating bots as a universal cure for review pressure. Read “Quality gatekeepers: investigating the effects of code review bots on pull request activities.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an agent PR workflow
When comparing workflows or tools, measure separate parts of the review process rather than treating them all as a single question of whether PRs are “fast.” Useful dimensions include:
- Scope: lines changed and files touched.
- Validation: test and CI failures.
- Queue timing: time to first human review and time to resolution.
- Review effort: revision churn and the work reviewers perform.
- Task fit: duplication, unwanted work and alignment with the original request.
- Explanation quality: whether the PR description explains intent and matches the diff.
These measures should remain distinct. A PR’s merge outcome, whether a human intervened, how long that intervention took, and a team’s overall review backlog are different outcomes. The cited research offers evidence about PR-level patterns, but not a general causal estimate of how agent adoption changes queue length or latency across organizations.
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