You should already have 2+ developers looking at most PRs. And these developers should absolutely use AI. The PR author should use AI.

But what you should not do is pipe the AI output directly into the PR and tell the PR author to deal with it. That's adding noise to the PR review process. Everything it says is something the PR author needs to validate as relevant, helpful, etc. A human needs to do that before confronting the author with it.

You wouldn't ask an agent to review a PR then just copy/paste the output into the PR, would you?

These models are still terrible compared to what we'd actually wish for, but they're the best available.

If you can get away with using the $200/mo subscriptions, it's really not even a money thing for most professionals.

Almost all of my work is now plan, generate, review, plan, generate, review, commit, push.

I'm using Claude or Codex (or both), and they're doing all of the testing "inline" rather than through a CI action, etc.

1. Run it in multiple cycles, only on the diff, and only emit a few findings at a time.

2. Give it a memory so each cycle, it knows the previous finding to check if it's been fixed.

3. Give it access to canonical docs that encode your human reviewer heuristics. I exposed these as tool calls so they could be tracked via telemetry.

4. Run multiple reviewers, each with a tight focus. Security, performance, structural, database, etc. Each a separate prompt and persona. Additionally, we had file activation filters so the FE React reviewer didn't activate on BE only changes.

Luna and 5.4-mini with no reasoning were exceptionally fast and almost always found issues with code produced by Opus and Fable.

Default prompts for the curious (these are templates deployed by default, but customizable).

Performance: https://github.com/zeeq-ai/zeeq-app/blob/main/src/backend/Ze...

Structural: https://github.com/zeeq-ai/zeeq-app/blob/main/src/backend/Ze...

(Keep in mind each agent also has tools to access and reference external docs.)

I am finding AI doing its own reviews as part of the process to be the key to productivity. I do subagent (fresh context reviews) at multiple stages with well-specified review criteria. It is really expensive to do with OpenAI or Claude API billing. Deepseek or the discounted monthly plans from OpenAI or Claude can be discounted similar to the 28x they state for Luna compared to Astra and you maintain much higher quality.

That's my way of saying, I am hesitant to trust a stupid model to do code review because I become complacent and when it suggests a small change that seems reasonable (the LLMs are very good at sounding reasonable, far better at sounding reasonable than being reasonable, in fact), I might not notice that it just did a stupid until much later, when it becomes a big pile of stupids.

My fault for trusting it, of course. But, my eyes glaze over when I read AI prose, whether it's code review or anything else. It's hard to catch one incorrect behavior in a batch of several reasonable suggestions.

We currently think it worth it. The review catches things humans and two paid options miss. It's definitely a wall of text and burnout fodder, the next step is an agent/skill that will make the changes after we humans comment on the comments, because the comment wall is not sustainable.

code reviews, unit tests, docs, whatever you dont want more expensive models working

i think the new muse contributor model is enticing too if you are not using it for private/sensitive stuff

I get remarkably good results using any recent OpenAI model using the codex-rs harness pointing at a built checkout of the PR. The models use the available tools (i.e. the shell) to understand the repo. I get some false positives and some false negatives, but I don’t believe for a second that I would get comparable performance using a dramatically less capable harness. (Also, the models read the short AGENTS.md for some context as to what’s going on. The prompt I use is about one sentence. I don’t bother with the built in review tool.)

(Also would have been nice if they included the equivalent Anthropic models for comparison as well, but it's not quite as relevant.)

https://blog.brokk.ai/mjolnir-automated-cross-vendor-adversa...

However, Luna missed 23 bugs that Astra found, and identified 24 bugs that weren't really bugs. That's horrible. Astra had 96% precision.

The cost to care about here isn't just how much it costs to run the code review, or the cost per true-positive. It's the cost of dealing with this system. A code review system that is right about 2/3 sucks, and one that misses another 1/3 of the bugs is also a lot worse. The Astra code review quoted here would become the foundation of how the team works, the Luna version is at best helpful to find some stuff but does not dramatically increase your confidence. It also will force humans or better AI's to have to run down a lot of false positives, and that is treated as free here.

Actual conclusion: The cost for Astra is low in absolute terms compared to the cost of bugs and human attention, and the added value is far far more than the added cost.

The pull requests are public, and the prompts, raw model outputs, judge verdicts, bug-class labels, repeat runs and scoring scripts are committed with this article.

Okay, but where are they? This quote says that PRs are public. Does that mean that everything else is private and we can’t actually reproduce those results?

This, and the fact that the article doesn't mention reasoning levels, sounds fishy to me.

The current link is a 404, seems like they didn't redirect it properly.

Would you like me to find a herbal formula against cocaine hangover?

I've seen this a few times on relatively simple changes on complex codebases.

Right now, Fable 5.1 delivers incredible reviews. Opus 5 delivers good reviews. These agents are finding really impressive issues that humans just don't have the attention span to track down. My reviewer has a very impressive signal to noise ratio at this point, after half a year of iterating and improving. (I use a lot of Opus high, Opus medium for less critical tickets/domains, Fable 5.1 high for critical domains and all of the issue validators, and even Fable 5.1 xhigh for my design agent, whose job it is to think about the project at a high level and provide the kind of high level tech design review that AI notoriously can't do well)

I'm testing Astra so I don't have strong opinions yet. I've also done extensive testing of the same skill and subagent pattern in opencode/omp using GLM 5.3, Kimi K3 max, Deepseek V4 pro, Deepseek V4.1 flash, Qwen 3.8 2.4T max, and others.

My experience is that open weights models find between 1/4 to 1/2 of what Fable/Opus stack can find, and often miss the most critical issues. I work where privacy isn't just good behavior, it's enforced by law, and the Fable/Opus stack has found privacy leaks that the openweights stacks don't find.

You can imagine that paying for these Claude runs isn't cheap, each one can eat 25-33% of my 5 hour limit. I am quite desperate for openweights models to be competitive, but at the end of the day, the biggest limit here isn't the price difference between GLM 5.3 max (my current best-in-class choice for open weights, offering Kimi k3 performance for like half the price), it's the cost to the business for shipping lower quality.

Can't wait to dig in more with Astra, I just haven't iterated much on my skill port to codex yet.

One criticsm I have for the article, that is important for my own work, is not simply comparing "bugs found" because these agents can find endless reams of lows and nitpicks that are just ~worthless hardening. I'd be much more interested to see how many critical/high/medium's each test found, not "overall bug count". I also think review is about A LOT more than "finding bugs"...