Cloudflare runs a multi-agent AI code reviewer on merge requests across 5,169 repositories
Cloudflare built a CI-native AI code review system around the open-source OpenCode agent, in which a coordinator agent manages up to seven specialised reviewers covering security, performance, code quality, documentation, release management and internal Engineering Codex compliance, with risk tiers, circuit breakers and model failback chains. Per the source, between 10 March and 9 April 2026 it completed 131,246 review runs across 48,095 merge requests, with a median review time of 3 minutes 39 seconds and an average cost of $1.19 per review.
Overview
Cloudflare built a CI-native AI code review system around the open-source OpenCode agent, in which a coordinator agent manages up to seven specialised reviewers covering security, performance, code quality, documentation, release management and internal Engineering Codex compliance, with risk tiers, circuit breakers and model failback chains. Per the source, between 10 March and 9 April 2026 it completed 131,246 review runs across 48,095 merge requests, with a median review time of 3 minutes 39 seconds and an average cost of $1.19 per review.
This entry has 10 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Code review is a good mechanism for catching bugs and sharing knowledge but can bottleneck an engineering team; across Cloudflare's internal projects the median wait for a first review was often measured in hours. Off-the-shelf AI review tools did not offer enough flexibility for an organisation of Cloudflare's size, and a naive prompt over a git diff was noisy.
The solution
Cloudflare built a CI-native orchestration system around OpenCode, an open-source coding agent. A coordinator agent manages up to seven specialised reviewers covering security, performance, code quality, documentation, release management and compliance with the internal Engineering Codex, deduplicates their findings, judges severity and posts a single structured review comment. Merge requests are classified into risk tiers, and circuit breakers and model failback chains handle provider failures.
Reported business value
Cloudflare reports that in the first 30 days (March 10 to April 9, 2026) the system completed 131,246 review runs across 48,095 merge requests in 5,169 repositories, with a median review time of 3 minutes and 39 seconds and an average cost of $1.19 per review. Engineers used the 'break glass' override on 288 merge requests (0.6%). These are company-reported figures.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other technology & software entries in the register.
HP crafts marketing campaigns that resonate with customers using Databricks and Uniphore
HP centralized first-party customer data on the Databricks Data + AI Platform with Delta Lake and Unity Catalog, and connected it to Uniphore's HybridCompute for federated query pushdown, cutting campaign setup from 2 weeks to 2 hours and processing 400 million records in seconds.
Building a safer and more sustainable world
Novade, a construction management software company, partnered with Databricks to modernize its analytics and ML infrastructure, moving from a homegrown Apache Airflow stack to the Databricks Data + AI Platform with Delta Lake, Unity Catalog and MLflow. The move reduced total cost of ownership by 60% and supported a 100% increase in new clients, while enabling ML models for predicting incident risk affecting worker safety and project delivery schedules.
SafeGraph optimizes geospatial data processing with Databricks and Delta Sharing
SafeGraph used the Databricks Data + AI Platform, Delta Lake, Delta Sharing and MLflow to process petabytes of geospatial data and feed predictive models, achieving 2x-10x faster spatial querying, indexing and partitioning and a 50% reduction in peak memory consumption compared to other platforms, while reducing data access time for partners from months to minutes via Delta Sharing.
Transforming Weather Forecasting with Lakeflow Jobs
AccuWeather migrated from on-premises infrastructure to Databricks and Lakeflow Jobs, working with Datadog for observability, to unify diverse weather data formats and orchestrate 4,500+ weekly jobs. Lakeflow Jobs coordinates the ingestion of multiple weather models, triggers machine learning processes that weight and blend different forecasts, and manages complex job dependencies for reinforcement training workflows used in AccuWeather's proprietary forecasting engine. AccuWeather reports 3x faster dataset development (three months to one month per dataset), a 50% reduction in unactionable alerts, and 50% cost savings on serverless job usage.
Was this helpful?
Your feedback helps us improve our use case database
