Would You Pay $1 for Agentic Code Review on PRs?<br>CrumpledPaper

Would You Pay $1 for Agentic Code Review on PRs? JP Hutchins Would You Pay $1 for Agentic Code Review on PRs?<br>July 19, 2026 · 15 min read<br>oss<br>ai<br>llm<br>github<br>ci-cd<br>code review

All posts written without LLM assistance unless otherwise noted.

It might surprise people that I’m investing time in LLM tooling — “Agentic” tooling even — because I’ve always been somewhat of an AI-skeptic. But, this code-review project is not my first foray into the category; I am also working on a task runner called camas that optionally includes an MCP and skills, and indeed it was used for the development of code-review. In turn camas now benefits from code-reviews’s agentic review on every PR.

Perhaps I am more of an “AI-industry” skeptic, or wary of hype and fear-mongering, than I am skeptical of LLMs themselves. I am not convinced that “AI agents” can replace engineers, or any occupation, really. I can only speak for my experience as a software engineer — the frontier models like Opus and Fable 5 are particularly well-suited for writing software, and on their own, I don’t think they’re that good at it. But I’m not sticking my head in the sand either. My experience is that LLMs, when bounded by expert judgement and deterministic checks, provide an incredible, albeit hard to quantify, boost to the pace of development and testing. One category where I’m very happy with their utility is code review, particularly agentic code review.

Lately, I’ve started to dread what a post-AI-bubble or post-token-subsidy world would look like. Will models that provide affordable inference survive? How will Open Source Software (OSS) projects benefit from LLMs if contributors are no longer able to use subsidized flat-rate plans?

#Agentic Code Review

Some definitions are in order since “agentic” is a bit of a buzz word.

#Peer Code Review

Let’s relate everything back to what code review means to you. In order to review the code, you must first read the code — additions and removals — and you may choose to also interact with the code. Interacting with the code would require copying (pulling) the changes to your local development environment and then fiddling with it: running the test suite, running the library or application, modifying code as you do so, and in my line of work — something that an LLM agent cannot do — flashing firmware to a physical device and interacting with it. The process uses aspects of engineering, design, quality assurance (QA), testing, and writing.

The kind of review that is read-only — an audit of changes — is like a “Static LLM Code Review” . A review that interacts with — or even modifies — the code is like an “Agentic LLM Code Review” .

#Static LLM Code Review

This is a relatively cheap approach to automated code review because it only reads. It’s also easy to implement: just pipe your git diff into an API chat prompt and ask for a review. There are many examples of ready made GitHub Actions to do this and it’s simple enough that you can do it yourself (something that will be a theme in this post.)

Some may claim that this is agentic because the chatbot could do things like make web searches to validate claims. Nonsense: those are other aspects of a standard read-only code review.

Still others might call an LLM doing this sort of review “static analysis”, to which I disagree fervently. Static analysis is something that software engineers are already familiar with and it is a deterministic routine to assess some qualities of the software. LLMs are stochastic not deterministic so they cannot perform what we traditionally consider static analysis…

#Agentic LLM Code Review

…unless those LLMs are given access to the deterministic tools that do perform static analysis. This is the realm of Agentic Code Review and it’s something that I have been quite impressed by ever since my first use of Claude Code’s absolute token incinerator of a command: /code-review.

An agentic code review uses a harness (like Claude Code) and can run compilers, linters, launch parallel subagents in worktrees, modify the code to confirm hypotheses, research dependencies, check for CVEs, possibly even interact with the application (though not on physical hardware for us firmware engineers 🤖).

Where a static LLM code review may come back in a few minutes after consuming less than 1M tokens, an agentic review may go on for tens of minutes, or even an hour, using 5M tokens and beyond. Which is why you’ll see Anthropic advertise this service as costing $15-$25 per PR. And frankly, I could see this going out to $100 eventually, based on the hour+ /code-review sessions I’ve run locally.

In my experience, these agentic reviews reliably surface real, sometimes quite subtle, findings. And that’s what we want. But how can we enable agentic code review at an affordable cost for OSS?

#Open Source Models

tl;dr it’s DeepSeek v4

I’m not going to claim to understand API token pricing,...