Code Review in the Age of AI-Generated Code

AI coding assistants have changed how software gets written. Code that once took a day can now take an hour. But faster writing creates a new challenge: someone still has to review all of it, carefully.

At OpenCore Group, speed matters to our clients. We shipped three years of Fraîche Table's product roadmap in one year, and we launched DentiMatch's new brand and website in 45 days. But speed is only valuable if what we ship is reliable. That's why AI code review has become a key part of how we work.

Why AI-generated code is different

AI-generated code usually looks clean and confident, even when it's wrong. Human mistakes often look messy, so they're easier to spot. AI mistakes hide behind good formatting.

AI also makes it easy to produce a lot of code quickly. When pull requests get bigger, reviewers tend to skim, and skimming is how bugs reach production.

Common problems we look for

• Edge cases. The main path works, but empty inputs, errors, or unusual data are handled poorly.
• Weak tests. When AI writes both the code and the tests, the tests often check what the code does, not what it should do.
• Made-up or outdated APIs. Functions that don't exist or don't match the library version in use.
• Unnecessary dependencies. New packages added without a clear reason.
• Security gaps. Missing permission checks, unsafe database queries, or exposed secrets.
• Duplicate code. New helpers that repeat logic already in the codebase.

How we review AI-generated code

The author owns the code. Whoever opens the pull request is responsible for every line, no matter who or what wrote it.

Keep pull requests small. One pull request should do one thing. Small changes get careful reviews; large ones get skimmed.

Review the tests first. Before reading the implementation, we check that the tests describe the behavior we actually want.

Use AI as a first reviewer, not the final one. AI review tools catch simple issues quickly. A human engineer always makes the final call.

Automate what machines do best. Linting, type checks, security scanning, and secret detection run on every pull request, so people can focus on logic and design.

Take extra care with sensitive code. Some software has no room for error. For AnesthesiaOne, a clinical reference app for anesthesia professionals, accuracy is essential. Changes to areas like this, as well as authentication, payments, and data, always get a reviewer with deep experience.

Why this matters for long-term products

Many of our client partnerships last for years. We've been the technical team behind eFundrs since 2023, shipping its roadmap year after year. For Page Flooring, we replaced five separate systems with one platform the business now relies on every day.

Products like these have to stay easy to maintain. Code that is rushed in today becomes expensive tomorrow. Careful review keeps a codebase clean, consistent, and ready for the next feature.

A simple checklist

Before approving an AI-assisted pull request, we ask:

1. Can the author explain every change?
2. Is the pull request focused on one task?
3. Do the tests cover real requirements and edge cases?
4. Do all libraries and APIs actually exist in the versions we use?
5. Are inputs validated and permissions checked?
6. Is existing code reused instead of duplicated?
7. Are errors handled properly?

What about AI agents that open pull requests?

Some AI agents can now open pull requests on their own. We treat them like a new team member: their work is welcome, but it goes through the same checks and review as everyone else's, and a named engineer is always responsible for the change. For more on running AI agents safely, read our guide to building production AI agents with MCP.

Frequently asked questions

Is AI-generated code safe to use in production? Yes, when it's reviewed properly. It needs the same testing, security checks, and human review as any other code.

Can AI replace human code reviewers? Not yet. AI tools catch common issues quickly, but people are still needed to judge design, business logic, and security.

Does careful code review slow down delivery? Not when it's done well. Small pull requests and automated checks keep reviews fast, and catching problems early saves time later.

Conclusion

AI helps teams write code faster, but quality depends on good review. Clear ownership, small pull requests, strong tests, and smart automation let us move quickly for our clients without lowering our standards.

Building a product that needs to move fast and stay reliable? Talk to OpenCore Group →