Orchestrating AI Code Review at Scale
Cloudflare developed a CI-native orchestration system for AI-powered code review to address scalability and flexibility limitations of existing tools. The system uses up to seven specialized AI agents to review code for security, performance, quality, and compliance, coordinated by a central agent that consolidates feedback. It has been deployed across tens of thousands of merge requests, improving review speed and accuracy while integrating into existing engineering workflows.
- ▪Cloudflare's AI code review system reduces bottlenecks by using specialized AI agents instead of relying on a single monolithic model.
- ▪The system is built on a composable plugin architecture that supports multiple version control systems and AI providers without tight coupling.
- ▪Specialized reviewers cover areas like security, performance, and compliance, with findings deduplicated and prioritized by a coordinator agent.
- ▪The orchestration system integrates with OpenCode, an open-source coding agent, to deliver structured review comments in the CI/CD pipeline.
- ▪It has been used internally at Cloudflare to automatically approve clean code and block merges with serious bugs or security issues.
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| Original publisher | The Cloudflare Blog |
| Canonical URL | https://blog.cloudflare.com/ai-code-review/ |
| Publication time | Wed, 29 Apr 2026 09:25:19 +0000 |
| Retrieval time | 2026-04-29T09:36:54.536Z |
| Last seen | 2026-04-29T09:36:54.536Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | VUZpnnDxlJo6 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Orchestrating AI Code Review at scale2026-04-20Ryan Skidmore19 min readThis post is also available in 简体中文, 日本語, 한국어 and 繁體中文.Code review is a fantastic mechanism for catching bugs and sharing knowledge, but it is also one of the most reliable ways to bottleneck an engineering team. A merge request sits in a queue, a reviewer eventually context-switches to read the diff, they leave a handful of nitpicks about variable naming, the author responds, and the cycle repeats. Across our internal projects, the median wait time for a first review was often measured in hours.When we first started experimenting with AI code review, we took the path that most other people probably take: we tried out a few different AI code review tools and found that a lot of these tools worked pretty well, and a lot…
Excerpt limited to ~120 words for fair-use compliance. The full article is at The Cloudflare Blog.
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