I ran an informal A/B test across 100 PRs, routing each through both AI and human review. AI caught 23 bugs humans missed. It also produced hundreds of useless comments. Here’s what the data actually says.
Ad-hoc AI usage creates inconsistency and hidden risk. Here’s how to build a practical standard: AGENTS.md, .cursorrules, prompt libraries, and review checklists — with real templates.
Cursor launched Automations on March 5, letting you set up always-on agents triggered by code changes, Slack messages, or PagerDuty alerts. After a week of testing, here’s what it actually looks like to manage a team where agents review every PR before a human even opens it.
I added CodeRabbit to our GitHub org on a Tuesday. By Friday, it had flagged 23 issues across 7 PRs — and my team thought I had secretly reviewed everything myself. After 30 days running four different AI PR review bots in parallel, here’s what actually caught real bugs versus what generated noise.
I use this code review checklist for tech leads to reduce false confidence and catch production-grade bugs: idempotency, timeout propagation, auth boundaries, and unsafe retries.
I ran the same 612-line TypeScript PR through five AI review agents to see which ones actually reduce reviewer workload. Two were consistently useful, two were mostly noisy, and one missed the highest-risk issue entirely. If you’re a tech lead choosing review tooling, this is the practical tradeoff breakdown.
A practical guide to using AI for code review — what actually works, what doesn’t, and where human judgment still matters.