Automating code review with Ona Automations

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Ona's code review automation has driven a 50% reduction in time spent on pull requests and an 88% acceptance rate on the agent's comments.

The problem

Code review is one of the biggest bottlenecks in the SDLC. Every PR needs human eyes, and those humans are usually the most senior and busiest engineers on the team. The review queue backs up, context switches pile on, and velocity suffers.

Most teams have tried AI-assisted code review tools to help, but the current generation has two fundamental limitations. First, they can only read the diff. They can't run the code, execute tests, or verify that the change actually works. This means their feedback is shallow: style suggestions and pattern matching rather than genuine validation. Second, they only see the code. They don't check whether the implementation actually meets the requirements described in the ticket, the design doc, or the broader project context.

The result is that AI review tools catch surface-level issues but miss the things human reviewers actually spend their time on: does this change do what it's supposed to, and does it work?

The solution

Ona Automations support PR-based triggers, which means you can configure an AI code review agent that runs automatically every time a pull request is opened or marked ready for review across your selected projects. The agent reviews the code, leaves comments, summarizes the changes, suggests fixes, and can even improve the PR before a human reviewer sees it.

Ona provides a code review automation template with a detailed, high-quality prompt that has been refined through extensive internal use and customer feedback. Teams can use it as-is or customize it to match their own review standards.

How the automation works

1. PR triggers the automation. When a pull request is opened or moved to "ready for review" on any of the configured projects, the automation fires. No manual step required.

2. The agent reviews the code in a full environment. This is where Ona's approach differs from other code review tools. Because the agent runs in a full dev container environment with access to the codebase, it can actually run the code and execute tests. If it finds an error, it can fix it. This produces significantly higher quality feedback than tools that only read the diff.

3. The agent checks requirements, not just code. Through integrations with Linear, Jira, Confluence, and Notion, the agent cross-references the PR against the original ticket and any linked documentation. It can flag cases where the code looks fine technically but doesn't meet the requirements described in the ticket. This is the kind of feedback that usually only comes from a human reviewer who has full project context.

4. The agent comments on the PR. The agent leaves a structured review on the pull request including a summary of the changes, inline comments on specific issues, suggestions for quick fixes it can handle, and an overall assessment. Engineers can respond to comments and the agent will engage in the conversation.

5. PRs get improved before human review. The automation can be configured to have the agent fix the issues it finds and push improvements to the PR directly. This means human reviewers only need to look at PRs that have already passed the AI quality gate, focusing their time on architecture and design decisions rather than catching obvious issues.

Why Ona's review agent is different

It runs on your infrastructure. The automation runs in your VPC, which means it works with on-prem Git solutions like self-hosted GitLab and GitHub Enterprise. Your code never leaves your network.

It runs the code, not just reads it. Full dev container and run loop support means the agent can execute the code, run the test suite, and verify changes actually work. If tests fail, it can diagnose and fix the issue before commenting. This produces deeper, more reliable feedback than tools that only perform static analysis on the diff.

It checks the full context. By pulling requirements from your project management and documentation tools, the agent provides holistic feedback that goes beyond code quality. A technically correct PR that doesn't implement what the ticket asked for gets flagged before a human wastes time reviewing it.

The result

Teams using Ona's code review automation have seen a 50% reduction in time spent on pull requests and an 88% acceptance rate on the agent's comments. This has materially increased engineering velocity by shifting human review time away from catching obvious issues and toward the architectural and design decisions that actually require human judgment.

The automation is also extensible. Teams have built on the same PR trigger to run automated security reviews every time a pull request is opened, catching vulnerabilities at the point of change rather than in periodic scans.

The template provides a strong starting point, and the prompt is fully customizable to align with your coding standards, review conventions, and project-specific requirements.

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