What is happening with code reviews?

Gergely Orosz · pragmaticengineer.com · Sep 8

The rapid rise of AI agents has triggered a fivefold surge in pull requests and code volume, creating a massive bottleneck for engineering teams and transforming traditional code reviews. CTOs and engineering leaders are struggling to keep up, leading to a variety of evolving approaches to manage the deluge of AI-generated code.

To cope with this influx, companies are adopting several distinct strategies: using AI tools to review code while humans review the feedback rather than the raw code; triaging changes by "blast radius" to route only high-risk updates to humans; shifting focus away from implementation to review upfront plans, tests, or database schemas instead; and prompting AI to generate smaller, more manageable pull requests. Ultimately, there is no one-size-fits-all solution, forcing teams to experiment with new automated workflows and robust guardrails to maintain quality without sacrificing development speed.

7 mins read · 1,755 words

Highlights from this article

Another common approach is to decide whether to review code by hand or with AI, based on how “risky” a change is:
• Low-risk change: only AI, without human review. It can ship to production once AI agents are happy
• High-risk change: mandatory human review
This is the approach that Anthropic and OpenAI follow

Sep 10 View on Readwise ↗
We decided to do a couple of things:
• Switch to a risk-based system. With a risk-based system, we agreed that if your change touched the public API/MCP, auth, design system, non-additive database schema changes, or agent skills, it needed a human review. We then enforced that with a shell script to add a GitHub label.
• Improve our guardrails (unit and end-to-end testing, post-deploy observability, stricter linting and type checking, etc). Improving guardrails was pretty easy, just expensive in tokens and attention. We enabled nearly every rule in ruff/prettier/eslint/ty, and we improved our unit test coverage to a floor of 85%.
Results before vs after:
• PRs merged: 353 → 684 (80/wk → 154/wk, +94%)
• Merged within 1h: 28% → 45%; within 24h: 76% → 80%
• Human-reviewed PRs median merge time: 26h
• No human-review median merge time: 1h.”

Sep 10 View on Readwise ↗
Uber’s custom-built Code Review Inbox highlights high-impact changes, so devs know to spend more time and effort on them:

Sep 10 View on Readwise ↗