# ai code review Published articles for ai code review. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Let coding agents review PRs without giving them unrestricted merge access DevFeed: [Let coding agents review PRs without giving them unrestricted merge access]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T15:29:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [AI-assisted coding](), [Code review](), [GitHub](), [Authorization](), [Pull Request](), [pull-requests]() Tags: [ai-code-review](), [authorization](), [code-review](), [coding-agents](), [github](), [reviews]() ### AI overview This guide explains how to let coding agents review GitHub pull requests without granting unrestricted merge access. It recommends separate review and merge credentials, explicit merge assignments governed by WorkOS Airlock, required checks and reviews, and reapproval when the reviewed commit changes. ### Source excerpt Set GitHub permissions for AI code review, govern merges with Airlock, and prevent agents from merging code that changed after approval. ## Harness shipped 58 features in August 2026, including AI code review and agent security scanning DevFeed: [Harness shipped 58 features in August 2026, including AI code review and agent security scanning]() Original publisher: [Read original article]() Author: Chinmay Gaikwad Published: 2026-09-02T18:04:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [AI Agent](), [Code review](), [prompt injection](), [Security](), [Terraform]() Tags: [2026](), [ai-code-review](), [code-review](), [coding-agents](), [opentofu](), [prompt-injection](), [security](), [terraform]() ### AI overview Harness describes 58 features released in August 2026, including an agent-scale code repository, AI Code Review, AI Risks scanning for prompt injection and tool poisoning in agent skills, and risk scoring for Terraform and OpenTofu changes. ### Source excerpt Harness shipped 58 features in August 2026: an agent-scale code repository, AI Code Review, AI Risks scanning, and the Blast Radius Agent. | Blog ## Introducing Agent-Ready Code Repository & AI Code Review DevFeed: [Introducing Agent-Ready Code Repository & AI Code Review]() Original publisher: [Read original article]() Author: Juveria Kanodia Colin Chartier Published: 2026-08-27T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [Pull Request](), [Code review]() Tags: [ai](), [ai-agents](), [ai-code-review](), [code-generation](), [code-review](), [software-delivery]() ### AI overview Harness introduces an agent-ready Code Repository and built-in AI Code Review to manage AI-generated code at high volume. The capabilities use risk-based diffs and scoped agent permissions to support governed software delivery before code merges. ### Source excerpt Legacy SCMs can't handle agent-scale code volume. Learn how Harness Code Repository and built-in AI Code Review handle AI-generated code at scale using risk-bas | Blog ## Experiments with AI Code Review DevFeed: [Experiments with AI Code Review]() Original publisher: [Read original article]() Author: Austin McKee Published: 2026-08-03T23:34:10Z Content type: article Language: en Sources: [Wealthfront]() Topics: [AI-assisted coding](), [Code review](), [Artificial Intelligence](), [Claude Code](), [Large Language Model](), [engineering-culture]() Tags: [ai](), [ai-code-review](), [ai-coding](), [anthropic](), [claude](), [claude-code](), [code-review](), [google](), [llm](), [openai](), [wealthfront-engineering]() ### AI overview Wealthfront describes a multi-year experiment using AI to augment code review. The article explains how an LLM caught a missed SQL filter that had passed automated testing and human code review, and why Wealthfront chose to build a structured review harness using models from Anthropic, OpenAI, and Google. ### Source excerpt Part of a series on Wealthfront's AI Developer Tooling Why Code Review? At Wealthfront, our arc of AI adoption has followed a familiar pattern: from skepticism to experimentation to deep adoption in the code lifecycle. Just a few years ago, in the dark era of "AI coding is just fancy autocomplete," we heard rumors of... Read more ## A Multi-Agent AI System for Reviewing Code Before Pull Requests DevFeed: [A Multi-Agent AI System for Reviewing Code Before Pull Requests]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-07-26T07:01:38Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [ai code review](), [Code review](), [Artificial Intelligence](), [Pull Request]() Tags: [ai-code-review](), [code-review](), [multi-agent](), [pull-request]() ### AI overview The author describes a multi-agent AI code review system that finds, verifies, and ranks issues before a pull request is opened. ### Source excerpt My multi-agent AI code review system finds, verifies, and ranks issues before I open the pull request. ## The growing code review workload and emerging AI-assisted approaches DevFeed: [The growing code review workload and emerging AI-assisted approaches]() Original publisher: [Read original article]() Author: Ivan Klaric Published: 2026-07-23T16:55:29Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [code reviews](), [ai code review](), [Software Engineering](), [Testing](), [Formal methods](), [GitHub]() Tags: [ai](), [ai-code-review](), [code-review](), [formal-methods](), [software](), [testing]() ### AI overview The article examines the growing code review workload as AI-generated code increases. It surveys dedicated AI review tools, review features in coding platforms, in-house systems, and verification approaches such as testing and formal methods, while noting that few solutions are proven. ### Source excerpt Top of mind for engineering leaders: what to do about the growing code review load, and how devs are starting to review code less thoroughly than before? Many questions, but few proven solutions. ## Neo code reviews: AI code review built for infrastructure DevFeed: [Neo code reviews: AI code review built for infrastructure]() Original publisher: [Read original article]() Author: Pulumi Neo Team Published: 2026-06-22T15:00:00Z Content type: release Language: en Sources: [Pulumi]() Topics: [pulumi-neo](), [ai code review](), [Infrastructure as code](), [pull-requests](), [GitHub]() Tags: [ai](), [ai-agents](), [ai-code-review](), [features](), [github](), [governance](), [infrastructure-as-code](), [logging](), [preview](), [product](), [pull-requests](), [pulumi-neo]() ### AI overview Pulumi introduces Neo code reviews in public preview. The feature analyzes GitHub pull requests alongside Pulumi Cloud infrastructure state, preview output, stack relationships, and dependencies to provide high-level and code-level feedback. It supports automatic or mention-triggered reviews, with governance controls including RBAC, guardrails, and audit logging. ### Source excerpt Today we're introducing Pulumi Neo code reviews, now in public preview. Neo code reviews analyze pull request changes in conjunction with what Pulumi Cloud knows about your running infrastructure, providing both high-level and code-level feedback. Normal code review agents can't reliably anticipate the impact an infrastructure-as-code change will have. This is because they don't have access to critical aspects of the IaC workflow: the potential impact the update will have, in this case the pulumi preview output; and the current state of the cloud infrastructure. Neo not only has access to both of those, but also to the entirety of your other cloud context, such as stack relationships and dependencies. Running reviews Neo can review every pull request automatically, or only when someone mentions @pulumi-neo. Either way, it skips draft pull requests and those opened by bots by default. A review is a comment, so it informs the person approving the merge and sits alongside the required checks and branch protection you already enforce. Neo code reviews run inside the same governance as every other Neo task, with the RBAC, guardrails, and audit logging your organization has set. Enable code reviews Neo code reviews are available on GitHub during public preview. They require Pulumi Neo to be enabled for your organization, the Pulumi GitHub App installed on the repositories you want reviewed, and a one-time grant from each organization user to access their GitHub account under Management > Version control. If Neo currently posts preview summaries on your pull requests, code reviews are already enabled, and they take the place of those summaries. Neo code reviews are free while in public preview. On July 1, 2026, they'll be generally available, and reviews will begin counting toward your organization's Neo token usage, at the same per-token rate as any other Neo task. The pricing page shows that rate and the monthly token allotment included with each plan. Give it a try Open ## With AI, The Proof Is in Production DevFeed: [With AI, The Proof Is in Production]() Original publisher: [Read original article]() Author: Joshua Klein Published: 2026-06-09T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [AI-assisted coding](), [feature flags](), [Software Engineering](), [Code](), [releases]() Tags: [ai](), [ai-code-review](), [code-review](), [feature-flags](), [metrics](), [progressive-delivery](), [safety](), [software]() ### AI overview The article argues that human review, AI code review, staging, and QA cannot guarantee that software changes will behave safely in production. Because AI-assisted development increases the volume and uncertainty of changes, it recommends relying on feature flags, progressive delivery, and production metrics to manage releases when review is imperfect. ### Source excerpt Human and AI code review can't guarantee production safety. Learn why feature flags, progressive delivery, and metrics-driven releases are essential in the AI software era. | Blog ## A Four-Layer Workflow for Reviewing AI-Generated Code DevFeed: [A Four-Layer Workflow for Reviewing AI-Generated Code]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-03-27T17:33:31Z Content type: tutorial Language: en Sources: [The AI Engineer]() Topics: [AI-assisted coding](), [Code review](), [Claude Code](), [coding](), [Pull Request](), [Shell]() Tags: [ai](), [ai-agents](), [ai-code-review](), [claude-code](), [code-review](), [coding](), [eslint](), [security](), [tests]() ### AI overview The article presents a four-layer workflow for reviewing AI-generated code: automated checks, local AI review, CI review on pull requests, and human review. It emphasizes using hooks in Claude Code to run linters and security scanners, executing the code, reading the diff, and using a fresh context for a second AI opinion. ### Source excerpt How to write better code when using AI agents ## Agentic Code Review: Pattern Matching for AI DevFeed: [Agentic Code Review: Pattern Matching for AI]() Original publisher: [Read original article]() Author: Robin Wieruch Published: 2026-03-18T06:50:46Z Content type: tutorial Language: en Sources: [Robin Wieruch]() Topics: [agentic-coding](), [Code review](), [AI Agent](), [engineering-culture](), [Code]() Tags: [agentic](), [agentic-code-review](), [agentic-coding](), [ai-agents](), [ai-code-review](), [ai-engineer](), [code-review](), [coding-patterns](), [github](), [review]() ### AI overview This article explains how teams can scale code review for AI-generated code by documenting project patterns and anti-patterns in structured files. AI agents can use the resulting reference document to review code against team conventions on GitHub and locally before pull requests are created. The approach depends on having consistent architecture and established conventions first. ### Source excerpt How to document project patterns and anti-patterns so AI agents can review code against your team's conventions automatically ... ## CodeRabbit for AI-Assisted Code Reviews on GitHub, GitLab, and Bitbucket DevFeed: [CodeRabbit for AI-Assisted Code Reviews on GitHub, GitLab, and Bitbucket]() Original publisher: [Read original article]() Author: TheAnkurTyagi Published: 2025-07-25T07:23:06Z Content type: opinion Language: en Sources: [ByteSizedBets]() Topics: [ai code review](), [Code review](), [AI-assisted coding](), [GitHub](), [bitbucket](), [GitLab]() Tags: [ai](), [ai-code-review](), [ai-coding](), [automation](), [bitbucket](), [code-review](), [github](), [gitlab]() ### AI overview The article argues that AI code review tools can automate repetitive first-pass checks and reduce review delays while keeping human reviewers responsible for context, judgment, and final decisions. ### Source excerpt A hands-on look at how CodeRabbit automates the boring checks so you can ship faster without cutting corners.