Last Thursday I was staring at a 612-line PR that touched auth middleware, a billing webhook, and a background retry worker. Exactly the kind of review that drains your attention by minute 20.
So instead of pretending I could “just focus harder,” I ran the same diff through five AI review agents and compared what they actually caught.
The short version: two tools saved me real time, two produced polished nonsense, and one was fast but missed the only issue I considered release-blocking.
Setup: One PR, Same Prompt, Same Rubric
I used one internal TypeScript PR (anonymized), then fed the same diff and prompt to each tool.
Quick caveat: this is a single-PR field test, not a controlled benchmark. The goal is practical reviewer signal for one realistic workflow, not universal model ranking.
Prompt:
Review this PR diff for:
1) correctness bugs
2) security/privacy risk
3) reliability issues (timeouts/retries/idempotency)
4) maintainability concerns that will hurt in 3+ months
Return only concrete findings with file + line context and severity.
Skip style nits.
Scoring rubric:
- High-signal findings: actionable and correct
- False positives: sounds serious but wrong
- Misses: issues I caught manually that tool skipped
- Time impact: how much review time it saved me in practice
What Each Agent Did Well (and Poorly)
1) Claude Code — Best risk framing, slower output
What it did well:
- Flagged a non-idempotent webhook path that could double-charge on retry
- Caught missing timeout propagation in a downstream HTTP call
- Explained failure mode clearly enough to paste into PR comments
Downsides:
- Tends to over-explain, so you still need to trim output
- Slowest response in my test set when diff size grew
- Sometimes proposes refactors bigger than what review scope needs
My take: best when I care about correctness and incident prevention, not speed.
2) GitHub Copilot PR Review — Fastest workflow fit, mixed depth
What it did well:
- Zero friction because comments show up where I already review
- Found two legitimate null-check gaps in changed files
- Good at quick triage on medium PRs
Downsides:
- Reliability findings were shallow (it rarely reasoned through retry semantics)
- Repeated one suggestion already covered by unit tests
- Signal dropped noticeably on cross-file logic
My take: excellent convenience tool, average deep reviewer.
3) Cursor Review Flow — Strong readability feedback, weaker security sense
What it did well:
- Spotted confusing function boundaries and naming debt quickly
- Suggested one extraction that made a 74-line function understandable
- Good at maintainability commentary without nitpicking whitespace
Downsides:
- Security analysis felt generic, almost checklist-level
- Missed a subtle auth context leak I expected it to catch
- Can produce too many “nice to have” suggestions in one pass
My take: good for code health, not my first line for risk-heavy changes.
4) CodeRabbit — Useful summaries, noisy edge-case warnings
What it did well:
- Nice sectioned summaries for large PRs
- Good at calling out test coverage gaps around modified code
- Helpful if your team wants structured bot comments by default
Downsides:
- Highest false-positive rate in my run
- Repeatedly warned about theoretical race conditions that weren’t real
- Comment volume can fatigue authors unless tuned aggressively
My take: workable with strict configuration, otherwise too chatty.
5) Aider + custom review prompt — Cheap and flexible, brittle ergonomics
What it did well:
- Lowest cost per run for repeated review passes
- Easy to tailor prompts for team-specific checklists
- Decent at finding straightforward correctness bugs
Downsides:
- More setup and context wrangling than integrated tools
- Output quality varied more between runs
- Easy to misuse if the operator prompt discipline is weak
My take: best for teams willing to invest in process, not plug-and-play.
The One Issue That Actually Mattered
The PR’s billing webhook handler updated invoice state before dedup verification. Under replay or provider retries, we could process the same event twice.
- Claude Code: flagged it as high severity with concrete replay scenario
- Copilot: hinted at “possible duplicate processing” but low confidence
- Cursor / CodeRabbit / Aider run: did not frame it as release-blocking
That single catch saved me from approving a bug that would have created messy refund cleanup. This is why I care less about total comment count and more about risk-weighted signal.
Time Saved (Realistic, not fantasy math)
My normal review time for this PR category is roughly 45-55 minutes.
With AI pre-pass + manual verification:
- Best case run (Claude + Copilot combo): about 29 minutes
- Noisiest run (CodeRabbit default settings): about 41 minutes after filtering
- Net savings: roughly 14-22 minutes, depending on tool noise
Not 10x. Still meaningful when you’re reviewing a queue every day.
So Which One Should a Tech Lead Standardize On?
If I had to pick a default stack today:
- Claude Code for risk-heavy or money-moving code paths
- Copilot PR Review for everyday throughput
- Cursor as a maintainability second opinion on ugly modules
I would not run all five on every PR. That’s performative, expensive, and slower than reviewing yourself.
Pick one primary reviewer and one fallback. Tune prompts once. Track false-positive rate monthly. That’s enough to get most of the benefit.
If you’re building your internal playbook, this also pairs well with my workflow in How I Use AI to Review Code 3x Faster, my reliability checklist in How I Debug Production Incidents With AI Under Pressure, and my broader team process in AI Sprint Planning: What Actually Works.
Final opinion
AI review agents are not “which model is smartest.” They’re “which tool gives your team the highest ratio of true risk findings to reviewer distraction.”
Right now, I trust AI most as a risk scanner and least as an auto-approver. If your team blurs that line, you’ll move faster for two weeks and then spend a month cleaning up preventable bugs.