Or, just spin up your own review workflow, I've been doing this for the past couple of months after experimenting with Greptile and it works pretty well, example setup below:

https://gist.github.com/rlueder/a3e7b1eb40d90c29f587a4a8cb7c...

An average of $0.04/review (200+ PRs with two rounds each approx.) total of $19.50 using Opus 4.6 over February.

It fills in a gap of working on a solo project and not having another set of eyes to look at changes.

> We've been running Code Review internally for months: on large PRs (over 1,000 lines changed), 84% get findings, averaging 7.5 issues. On small PRs under 50 lines, that drops to 31%, averaging 0.5 issues. Engineers largely agree with what it surfaces: less than 1% of findings are marked incorrect.

So the take would be that 84% heavily Claude driven PRs are riddled with ~7.5 issues worthy bugs.

Not a great ad of agent based development quality.

Interesting: "Reviews are billed on token usage and generally average $15–25, scaling with PR size and complexity."

Interesting to see this formalized. I've been running controlled experiments on why context separation improves LLM review quality — something I'm calling Cross-Context Review (CCR).

Setup: 30 artifacts (code, docs, scripts), 150 injected errors, 4 review conditions, 360 total reviews using Claude Opus 4.6.

Results:

- Cross-Context Review (artifact only, no production history): F1 28.6%

- Same-session self-review: F1 24.6% (p=0.008 vs CCR)

- Same-session repeated review (SR2): F1 21.7%

The SR2 result is the key finding — reviewing twice in the same session doesn't help (p=0.11 vs single review). The model generates more noise, not more signal. This rules out "two looks are better than one" as an explanation. It's the context separation itself that matters.

The gap is widest on critical errors: 40% detection for CCR vs 29% for same-session review.

Mechanism: production context introduces anchoring bias + sycophancy + context rot. A fresh session eliminates all three simultaneously by removing the conditioning tokens.

What Anthropic is doing here — dispatching independent agents that never saw the production context — is essentially this principle at industrial scale. Working on a paper but not published yet.

Yeah, the "$15-20 a PR is cheaper than a great engineer" idea is doing a lot of hand‑waving here...

If you're a big shop pushing, say, 2,000 PRs a week and reviews average $15–25, that’s on the order of $30k–$50k a week in AI review spend, or $1.5-2.5M a year. That is quite a line item to justify.

"It's $20 cheaper than a senior engineer’s hourly rate,"... so what are you actually doing with your human reviewers once you add this on?

If you keep your existing review culture and just bolt this on, then you've effectively said "we’re willing to add $1–2M+ a year to the budget." That might be fine, but then you should be able to point to fewer incidents, shorter lead times, higher coverage, something like that.

Either this is a replacement story (fewer humans, different risk profile) or it's an augmentation story (same humans, bigger bill, hopefully better outcomes). "It’s cheaper than a great engineer" by itself skips over the fact that, at scale, you’re stacking this cost on top of the engineers you already have in the org.

When a tool flags 8 issues on clean code and 8 issues on broken code, it's not a reviewer, it's a random number generator with a UI. The approach we've found more tractable is to separate concerns: let deterministic tools (linters, SAST, SCA) handle what they're definitively good at - style, known vuln patterns, dependency CVEs, secrets and reserve the AI layer for things humans actually need help reasoning about. Running this locally as a pre-push or CI step means you catch the boring 80% before it ever reaches a $25 AI review. You're not paying Claude to tell you your import is unused - you're paying it to reason about whether your auth flow has a TOCTOU issue. That's a very different and much more valuable question.

I'm curious how this compares to just setting up a claude-code-action with one of Anthropic's existing code-review plugins:

* https://github.com/anthropics/claude-plugins-official/tree/m...

* https://github.com/anthropics/claude-plugins-official/tree/m...

So their business model is to deliver me buggy code and then charge me to fix it?

> Reviews are billed on token usage and generally average $15–25, scaling with PR size and complexity.

You've got to be completely insane to use AI coding tools at this point.

This is the subsidised cost to get users to use it, it could trivially end up ten times this amount. Plus, you've got the ultimate perverse incentive where the company that is selling you the model time to create the PRs is also selling you the review of the same PR.

The concept of "AI will review AI-authored PRs" seems completely wrong to me. Why didn't the AI write the correct code in the first place?

If it takes 17 rounds of review from 5 different models/harnesses – I don't care. Just spit out the right code the first time. Otherwise I'm wasting my time clicking "review this" over and over until the PR is worth actually having a human look at.

what are the implications for the tens of code review platforms that have recently raised on sky high valuations?

I shipped this parallel agent workflow with validator agents as an internal tool months ago. It got a 20% reduction in time-to-merge, had a similar cost, and left 10x the comments as any existing AI review tool.

It's totally worth it.

My experience has been similar. LLM reviews are useful, but they tend to always produce findings. Even on small or very clean changes you wll still get a list of suggestions.

So part of the workflow becomes filtering signal vs noise.

nice but why is this not a system prompt? what's the value add here?

Does AI review of AI generated code even make sense?