For designers who code but aren't senior engineers, submitting pull requests can be daunting. Using an integrated AI code review agent provides an extra layer of validation. It catches potential issues and suggests improvements, boosting confidence before the code undergoes human review.

Most developers admit to giving pull requests only a cursory glance rather than pulling down the code, testing it, and reviewing every line. AI agents are perfectly suited for this meticulous, time-consuming task, promising a new level of rigor in the code review process.

When an AI model generates code, the focus of a pull request review changes. It's no longer just about whether the code works. The engineer must now explain and defend the architectural choices the model made, demonstrating they understand the implications and haven't just accepted a default, suboptimal solution.

To scale code review with 8x output, teams should codify and check-in their standards—specs, design systems, style guides—into the repository. AI reviewers can then automatically validate new code against this explicit "statement of what good looks like," reducing the burden on human reviewers.

A surprising side effect of using AI at OpenAI is improved code review quality. Engineers now use AI to write pull request summaries, which are consistently more thorough and better at explaining the 'what' and 'why' of a change. This improved context helps human reviewers get up to speed faster.

To meet compliance rules requiring a human approver, an AI bot can perform the full review and signal its approval with a gray checkmark. This transforms the human's role from a time-consuming reviewer into a final approver who can confidently merge the code in two clicks.

Contrary to fears that AI creates low-quality "slop," Intercom found their code quality improved. AI compresses the cost of fixing tech debt, flaky tests, and other internal projects, making it easier for the business to invest in them.

AI agents can generate and merge code at a rate that far outstrips human review. While this offers unprecedented velocity, it creates a critical challenge: ensuring quality, security, and correctness. Developing trust and automated validation for this new paradigm is the industry's next major hurdle.

To maintain quality while increasing shipping velocity, Snap uses an AI agent named CodeBal for the first pass of all code reviews. It understands the entire codebase of millions of lines, catching complex issues humans might miss and significantly speeding up the development cycle.