You can always tell claude to use red-green-refactor and that really is a step-up from "yeah don't forget to write tests and make sure they pass" at the end of the prompt, sure. But even better, tell it to create subagents to form red team, green team and refactor team while the main instance coordinates them, respecting the clean-room rules. It really works.
The trick is just not mixing/sharing the context. Different instances of the same model do not recognize each other to be more compliant.
> But even better, tell it to create subagents to form red team, green team and refactor team while the main instance coordinates them, respecting the clean-room rules. It really works.
It helps, but it definitely doesn't always work, particularly as refactors go on and tests have to change. Useless tests start grow in count and important new things aren't tested or aren't tested well.
I've had both Opus 4.6 and Codex 5.3 recently tell me the other (or another instance) did a great job with test coverage and depth, only to find tests within that just asserted the test harness had been set up correctly and the functionality that had been in those tests get tested that it exists but its behavior now virtually untested.
Reward hacking is very real and hard to guard against.
The trick is, with the setup I mentioned, you change the rewards.
The concept is:
Red Team (Test Writers), write tests without seeing implementation. They define what the code should do based on specs/requirements only. Rewarded by test failures. A new test that passes immediately is suspicious as it means either the implementation already covers it (diminishing returns) or the test is tautological. Red's ideal outcome is a well-named test that fails, because that represents a gap between spec and implementation that didn't previously have a tripwire. Their proxy metric is "number of meaningful new failures introduced" and the barrier prevents them from writing tests pre-adapted to pass.
Green Team (Implementers), write implementation to pass tests without seeing the test code directly. They only see test results (pass/fail) and the spec. Rewarded by turning red tests green. Straightforward, but the barrier makes the reward structure honest. Without it, Green could satisfy the reward trivially by reading assertions and hard-coding. With it, Green has to actually close the gap between spec intent and code behavior, using error messages as noisy gradient signal rather than exact targets. Their reward is "tests that were failing now pass," and the only reliable strategy to get there is faithful implementation.
Refactor Team, improve code quality without changing behavior. They can see implementation but are constrained by tests passing. Rewarded by nothing changing (pretty unusual in this regard). Reward is that all tests stay green while code quality metrics improve. They're optimizing a secondary objective (readability, simplicity, modularity, etc.) under a hard constraint (behavioral equivalence). The spec barrier ensures they can't redefine "improvement" to include feature work. If you have any code quality tools, it makes sense to give the necessary skills to use them to this team.
It's worth being honest about the limits. The spec itself is a shared artifact visible to both Red and Green, so if the spec is vague, both agents might converge on the same wrong interpretation, and the tests will pass for the wrong reason. The Coordinator (your main claude/codex/whatever instance) mitigates this by watching for suspiciously easy green passes (just tell it) and probing the spec for ambiguity, but it's not a complete defense.
This is very interesting, but like sibling comments, I'm very curious as to how you run this in practice. Do you just tell Claude/Copilot to do what you describe?
AFAIK Claude doesn't support it, but if you're willing to go the extra mile, you can get creative with some bash script: https://pastebin.com/raw/m9YQ8MyS (generated this a second ago - just to get the point across )
To be clear, I don't do this. I never saw an agent cheat by peeking or something. I really did look through their logs.
I'd be very interested to see claude code and other tools support this pattern when dispatching agents to be really sure.
I'm noticing terms related to DL/RL/NLP are being used more and more informally as AI takes over more of the cultural zeitgeist and people want to use the fancy new terms of the era, even if inaccurately. A friend told me he "trained and fine tuned a custom agent" for his work when what he meant was he modified a claude.md file.
There is a nontrivial amount of RL training (RLHF, RLVR, ...), so it would be reasonable to call it an RL model.
And with that comes reward hacking - which isn't really about looking for more reward but rather that the model has learned patterns of behavior that got reward in the train env.
That is, any kind of vulnerability in the train env manifests as something you'd recognize as reward hacking in the real world: making tests pass _no matter what_ (because the train env rewarded that behavior), being wildly sycophantic (because the human evaluators rewarded that behavior), etc.
> Is it really about rewards? Im genuinely curious. Because its not a RL model.
Ha, good point. I was using it informally (you could handwave and call it an intrinsic reward if a model is well aligned to completing tasks as requested), but I hadn't really thought about it.
It can if your refactor needs to deal with interface changes, like moving methods around, changing argument order etc... all these need to propagate to the tests
Your tests are an assertion that 'no matter what this will never change'. If your interface can change then you are testing implementation details instead of the behavior users care about.
the above is really hard. A lot of tdd 'experts' don't understand is and teach fragile tests that are not worth having.
It depends on what you mean by "refactor" and how exactly you're testing, I guess, but that's not really at the heart of the point. red-green-refactor could also be used for adding new features, for instance, or an entire codebase, I guess.
I’m telling it to use red/green tdd [1] and it will write test that don’t fail and then says “ah the issue is already fixed” and then move on. You really have to watch it very closely. I’m having a huge problem with bad tests in my system despite a “governance model” that I always refer it to which requires red/green tdd.
What has worked better for me is splitting authority, not just prompts. One agent can touch app code, one can only write failing tests plus a short bug hypothesis, and one only reviews the diff and test output. Also make test files read only for the coding agent. That cuts out a surprising amount of self-grading behavior.
It’s not an agentic pattern, it’s an approach to test driven development.
You write a failing test for the new functionality that you’re going to add (which doesn’t exist yet, so the test is red). You then write the code until the test passes (that is, goes green).
Good idea, and an improvement, but you still have that fundamental issue: you don't really know what code has been written. You don't know the refactors are right, in alignment with existing patterns etc.
thats a great idea - i have been using codex to do my code reviews since i have it to give better critique on code written by claude but havent tried it with testing yet!
codex/gpt is a stubborn model, doubt it would accept claude reviews or counter it. have seen cases where claude is more willing to comply if shared feedback though its just sycophancy too.
Am I supposed to be impressed by this? I think people are now just using agents for the sake of it. I'm perfectly happy running two simple agents, one for writing and one for reviewing. I don't need to go be writing code at faster than light speed. Just focusing on the spec, and watching the agent as it does its work and intervening when it goes sideways is perfectly fine with me. I'm doing 5-7x productivity easily, and don't need more than that.
I also spend most of my time reviewing the spec to make sure the design is right. Once I'm done, the coding agent can take 10 minutes or 30 minutes. I'm not really in that much of a rush.
I went the same way. At first I was splitting off work trees and running all the agents that I could afford, then I realized I just can't keep up with it all, running few agents around one issue in one directory is fast enough. Way faster than before and I can still follow what's happening.
> off work trees and running all the agents that I could afford,
I still think that we, programmers, having to pay money in order to write code is a travesti. And I'm not talking about paying the license for the odd text editor or even for an operating system, I'm talking about day-to-day operations. I'm surprised that there isn't a bigger push-back against this idea.
I've been doing differential testing in Gemini CLI using sub-agents. The idea is:
1. one agent writes/updates code from the spec
2. one agent writes/updates tests from identified edge cases in the spec.
3. a QA agent runs the tests against the code. When a test fails, it examines the code and the test (the only agent that can see both) to determine blame, then gives feedback to the code and/or test writing agent on what it perceives the problem as so they can update their code.
(repeat 1 and/or 2 then 3 until all tests pass)
Since the code can never fix itself to directly pass the test and the test can never fix itself to accept the behavior of the code, you have some independence. The failure case is that the tests simply never pass, not that the test writer and code writer agents both have the same incorrect understanding of the spec (which is very improbable, like something that will happen before the heat death of the universe improbable, it is much more likely the spec isn't well grounded/ambiguous/contradictory or that the problem is too big for the LLM to handle and so the tests simply never wind up passing).
This was really good, and second leaning on property testing. I’ve had really good outcomes from setting up Schemathesis and getting blanket coverage for stuff like “there should be no request you can generate as logged in user A that let’s you do things as or see things that belong to user B”, as well as “there should be no request you can find to any API endpoint that can trigger a 5xx response”
It's... really the same problem when you hire people to just write tests. A lot of time it just confirms that the code does what the code does. Having clear specs of what the code should do make things better and clearer.
Yep, tests written after the fact are just verifying tautologies.
> Most teams don't [write tests first] because thinking through what the code should do before writing it takes time they don't have.
It's astonishing to me how much our industry repeats the same mistakes over and over. This doesn't seem like what other engineering disciplines do. Or is this just me not knowing what it looks like behind the curtain of those fields?
That's probably dependent on your specific area of work. For most projects, It's okayish to deploy code with bugs. There will be future releases that fix those bugs and add improvements. Obviously that's not the case with high risk systems like space rockets software and similar.
With other engineering professions, all projects are like that. You cannot "deploy a bridge to production" to see what happens and fix it after a few have died
When push comes to shove, software can usually be fudged. Unlike a building or a water treatment plant where the first fuck up could mean that people die.
I like to think that people writing actual mission critical software try their absolute best to get it right before shipping and that the rest our industry exists in a totally separate world where a bug in the code is just actually not that big of a deal. Yeah, it might be expensive to fix, but usually it can be reverted or patched with only an inconvenience to the user and to the business.
It’s like the fines that multinational companies pay when breaking the law. If it’s a cost of doing business, it’s baked into the price of the product.
You see this also in other industries. OSHA violations on a residential construction site? I bet you can find a dozen if you really care to look. But 99% of the time, there are no consequences big enough for people to care so nobody wears their PPE because it “slows them down” or “makes them less nimble”. Sound familiar?
a lot of the value of tests is confirming that the system hasn't regressed beyond the behavior at the original release. It's bad if the original release is wrong, but a separate issue is if the system later accidentally stops behaving the way it did originally.
The issue I see is that the high test coverage created by having LLMs write tests results in almost all non-trivial changes breaking tests, even if they don't change behavior in ways that are visible from the outside. In one project I work, we require 100% test coverage, so people just have LLMs write tons of tests, and now every change I make to the code base always breaks tests.
So now people just ignore broken tests.
> Claude, please implement this feature.
> Claude, please fix the tests.
The only thing we've gained from this is that we can brag about test coverage.
Unit tests vs acceptance tests. You shouldn't be afraid to throw away unit tests if the implementation changes, and acceptance tests should verify behavior at API boundaries, ignoring implementation details.
yup agree - i think have specs and then do verifications against the spec. I have heard that this is how a lot of consulting firms work - you have acceptance criterias and thats how work is validated.
You can find approaches that improve things, but there's always going to be a chance that your code is terrible if you let an LLM generate it and don't review it with human eyes.
But review fatigue and resulting apathy is real. Devs should instead be informed if incorrect code for whatever feature or process they are working on would be high-risk to the business. Lower-risk processes can be LLM-reviewed and merged. Higher risk must be human-reviewed.
If the business you're supporting can't tolerate much incorrectness (at least until discovered), than guess what - you aren't going to get much speed increase from LLMs. I've written about and given conference talks on this over the past year. Teams can improve this problem at the requirements level: https://tonyalicea.dev/blog/entropy-tolerance-ai/
Sounds like we've just gotten into lazy mode where we believe that whatever it spits out is good enough. Or rather, we want to believe it, and convince ourselves that some simple guardrail we put up will make it true, because God forbid we have to use our own brain again.
What if instead, the goal of using agents was to increase quality while retaining velocity, rather than the current goal of increasing velocity while (trying to) retain quality? How can we make that world come to be? Because TBH that's the only agentic-oriented future that seems unlikely to end in disaster.
One thing I've been wrestling with building persistent agents is memory quality. Most frameworks treat memory as a vector store — everything goes in, nothing gets resolved. Over time the agent is recalling contradictory facts with equal confidence.
The architecture we landed on: ingest goes through a certainty scoring layer before storage. Contradictions get flagged rather than silently stacked. Memories that get recalled frequently get promoted; stale ones fade.
It's early but the difference in agent coherence over long sessions is noticeable. Happy to share more if anyone's going down this path.
Pet peeve: this post misunderstands “TDD.” What it really describes is acceptance tests.
TDD is a tool for working in small steps, so you get continuous feedback on your work as you go, and so you can refine your design based on how easy it is to use in practice. It’s “red green refactor repeat”, and each step is only a handful of lines of code.
TDD is not “write the tests, then write the code.” It’s “write the tests while writing the code, using the tests to help guide the process.”
> TDD is a tool for working in small steps, so you get continuous feedback on your work as you go, and so you can refine your design based on how easy it is to use in practice.
I would like to emphasize that feedback includes being alerted to breaking something you previously had working in a seemly unrelated/impossible way.
I guess to reach this point you have already decided you don't care what the code looks like.
Something I'm starting to struggle with is when agents can now do longer and more complex tasks, how do you review all the code?
Last week I did about 4 weeks of work over 2 days first with long running agents working against plans and checklists, then smaller task clean ups, bugfixes and refactors. But all this code needs to be reviewed by myself and members from my team. How do we do this properly? It's like 20k of line changes over 30-40 commits. There's no proper solution to this problem yet.
One solution is to start from scratch again, using this branch as a reference, to reimplement in smaller PRs. I'm not sure this would actually save time overall though.
Redoing the work as smaller PRs might help with readability, but then you get the opposite problem: it becomes hard to hold all the PRs in your head at once and keep track of the overall purpose of the change (at least for me).
IMO the real solution is figuring out which subset of changes actually needs human review and focusing attention there. And even then, not necessarily through diffs. For larger agent-generated changes, more useful review artifacts may be things like design decisions or risky areas that were changed.
So you have become a reviewer instead of a programmer? Is that so? hones question. And if so, what is the advantage of looking a code for 12 hours instead of coding for 12.
It sounds like you know this but what happened is that you didn't do 4 weeks of work over 2 days, you got started on 4 weeks of work over 2 days, and now you have to finish all 4 weeks worth of work and that might take an indeterminate amount of time.
If you find a big problem in commit #20 of #40, you'll have to potentially redo the last 20 commits, which is a pain.
You seem to be gated on your review bandwidth and what you probably want to do is apply backpressure - stop generating new AI code if the code you previously generated hasn't gone through review yet, or limit yourself to say 3 PRs in review at any given time. Otherwise you're just wasting tokens on code that might get thrown out. After all, babysitting the agents is probably not 'free' for you either, even if it's easier than writing code by hand.
Of course if all this agent work is helping you identify problems and test out various designs, it's still valuable even if you end up not merging the code. But it sounds like that might not be the case?
Ideally you're still better off, you've reduced the amount of time being spent on the 'writing the PR' phase even if the 'reviewing the PR' phase is still slow.
yeah honestly thats what i am struggling with too and I dont have a a good solution. However, I do think we are going to see more of this - so it will be interesting to see how we are going to handle this.
i think we will need some kind of automated verification so humans are only reviewing the “intent” of the change. started building a claude skill for this (https://github.com/opslane/verify)
Code review is a skill, as is reading code. You're going to quickly learn to master it.
> It's like 20k of line changes over 30-40 commits.
You run it, in a debugger and step through every single line along your "happy paths". You're building a mental model of execution while you watch it work.
> One solution is to start from scratch again, using this branch as a reference, to reimplement in smaller PRs. I'm not sure this would actually save time overall though.
Not going to be a time saver, but next time you want to take nibbles and bites, and then merge the branches in (with the history). The hard lesson here is around task decomposition, in line documentation (cross referenced) and digestible chunks.
But if you get step debugging running and do the hard thing of getting through reading the code you will come out the other end of the (painful) process stronger and better resourced for the future.
Oh I didn't mean literally how do I review code. I meant, if an agent can write a lot of code to achieve a large task that seemingly works (from manual testing), what's the point if we haven't really solved code review? There's still that bottleneck no matter how fast you can get working code down.
>Last week I did about 4 weeks of work over 2 days first with long running agents working against plans and checklists, then smaller task clean ups, bugfixes and refactors. But all this code needs to be reviewed by myself and members from my team. How do we do this properly? It's like 20k of line changes over 30-40 commits. There's no proper solution to this problem yet.
Get an LLM to generate a list of things to check based on those plans (and pad that out yourself with anything important to you that the LLM didn't add), then have the agents check the codebase file by file for those things and report any mismatches to you. As well as some general checks like "find anything that looks incorrect/fragile/very messy/too inefficient". If any issues come up, ask the agents to fix them, then continue repeating this process until no more significant issues are reported. You can do the same for unit tests, asking the agents to make sure there are tests covering all the important things.
He admits the real hole himself: "this doesn't catch spec misunderstandings. If your spec was wrong to begin with, the checks will pass."
But there's a second problem underneath that one. Acceptance criteria are ephemeral. You write them before prompting, Playwright runs against them, and then where do they go? A Notion doc. A PR comment. Nowhere permanent. Next time an agent touches that feature, it's starting from zero again.
The commit that ships the feature should carry the criteria that verified it. Git already travels with the code. The reasoning behind it should too.
Nope - though I’ll take it as a compliment either way. It’s a problem I’ve been sitting with for a while, so the answer came out more formed than I expected. You disagree?
Our app is a desktop integration and last year we added a local API that could be hit to read and interact with the UI. This unlocked the same thing the author is talking about - the LLM can do real QA - but it's an example of how it can be done even in non-web environments.
Edit: I even have a skill called release-test that does manual QA for every bug we've ever had reported. It takes about 10 hours to run but I execute it inside a VM overnight so I don't care.
They're definitely inferior to proper tests, but even weak CC tests on top of CC code is an improvement over no tests. If CC does make a change that shifts something dramatically even a weak test may flag enough to get CC to investigate.
Even better though - external test suits. Recently made a S3 server of which the LLM made quick work for MVP. Then I found a Ceph S3 test suite that I could run against it and oh boy. Ended up working really good as TDD though.
yeah i have been hearing a lot more about this concept of “digital twins” - where you have high fidelity versions of external services to run tests against. You can ask the API docs of these external services and give it to Claude. Wonder if that is where we will be going more towards.
Wasn't the best practice to run one model/coding agent that writes the code and another one that reviews it? E.g. Claude Code for writing the code, GPT Codex to review/critique it? Different reward functions.
> When Claude writes tests for code Claude just wrote, it's checking its own work.
You can have Gemini write the tests and Claude write the code. And have Gemini do review of Claude's implementation as well. I routinely have ChatGPT, Claude and Gemini review each other's code. And having AI write unit tests has not been a problem in my experience.
yeah i have started using codex to do my code reviews and it helps to have “a different llm” - i think one of my challenges has been that unit tests are good but not always comprehensive. you still need functional tests to verify the spec itself.
I wish there was a way to "freeze" the tests. I want to write the tests first (or have Claude do it with my review), and then I want to get Claude to change the code to get them to pass - but with confidence that it doesn't edit any of the test files!
I use devcontainers in all the projects I use claude code on. [1] With it you can have claude running inside a container with just the project's code in write access and also mount a test folder with just read permissions, or do the opposite. You can even have both devcontainers and run them at the same time.
Are there really no ways to control read/write permissions in a smart way? I've not had to do this yet, but is it really only capable of either being advisory with you implementing all the code, or it having full control over the repo where you just hope nothing important is changed?
You could probably make a system-level restriction so the software physically can't modify certain files, but I'm not sure how well that's going to fly if the program fails to edit it and there's no feedback of the failure.
You can use a Claude PreToolUse command hook to prevent write (or even read) access to specific files.
With this approach you can enforce that Claude cannot access to specific files. It’s a guarantee and will always work, unlike a prompt or Claude.md which is just a suggestion that can be forgotten or ignored.
This post has an example hook for blocking access to sensitive files:
Why not use a client-server infrastructure for tests? The server sends the test code, the client runs the code, sends the output to the server and this replies pass/not pass.
One could even make zero-knowledge test development this way.
yeah i agree - this is somewhat the approach I have been using more of. Write the tests first based on specs and then write code to make the tests pass. This works well for cases where unit tests are sufficient.
I'm not up to speed on Claude's features. Can I, from the prompt, quickly remove those permissions and then re-add them (i.e. one command to drop, and one command to re-add)?
Yeah, you can type `/permisssions` and do it there. Or you can make a custom slash command, or just ask Claude to do it. You can also set it when you launch a claude session, there are a dozen ways to do anything.
Just tell it that the tests can't be changed. Honestly I'd be surprised if it tried to anyway. I've never had it do that through many projects where tests were provided to drive development.
> Changes land in branches I haven't read. A few weeks ago I realized I had no reliable way to know if any of it was correct: whether it actually does what I said it should do.
I care about this. I don't want to push slop, and I had no real answer.
That’s really putting the cart before the horse. How do you get to “merging 50 PRs a week” before thinking “wait, does this do the right thing?”
Yeah just wanted to see what the bottlenecks would be as I started pushing the limits. Eventually made this into a verification skill(github.com/opslane/verify)
I am afraid that we are heading to a world in which we simply give up on the idea of correct code as an aspiration to strive for. Of course code has always been bad, and of course good code has never been a goal in the whole startup ecosystem (for perfectly legitimate reasons!). But that real production code, for services that millions or even billions of people rely on, should be reliable, that if it breaks that's a problem, this is the whole _engineering_ part of software engineering. And we can say: if we give that up we're going to have a whole lot more outages, security issues, all those things we are meant to minimize as a profession. And the answer is going to be: so what? We save money overall. And people will get used to software being unreliable; which is to say, people will not have a choice but to get used to it.
Feels like a whole bunch of us are converging on very similar patterns right now.
I've been building OctopusGarden (https://github.com/foundatron/octopusgarden), which is basically a dark software factory for autonomous code generation and validation. A lot of the techniques were inspired by StrongDM's production software factory (https://factory.strongdm.ai/). The autoissue.py script (https://github.com/foundatron/octopusgarden/blob/main/script...) does something really close to what others in this thread are describing with information barriers. It's a 6-phase pipeline (plan, review plan, implement, cold code review, fix findings, CI retry) where each phase only gets the context it actually needs. The code review phase sees only the diff. Not the issue, not the plan. Just the diff. That's not a prompt instruction, it's how the pipeline is wired. Complexity ratings from the review drive model selection too, so simple stuff stays on Sonnet and complex tasks get bumped to Opus.
On the test freezing discussion, OctopusGarden takes a different approach. Instead of locking test files, the system treats hand-written scenarios as a holdout set that the generating agent literally never sees. And rather than binary pass/fail (which is totally gameable, the specification gaming point elsewhere in this thread is spot on), an LLM judge scores satisfaction probabilistically, 0-100 per scenario step. The whole thing runs in an iterative loop: generate, build in Docker, execute, score, refine. When scores plateau there's a wonder/reflect recovery mechanism that diagnoses what's stuck and tries to break out of it.
The point about reviewing 20k lines of generated code is real. I don't have a perfect answer either, but the pipeline does diff truncation (caps at 100KB, picks the 10 largest changed files, truncates to 3k lines) and CI failures get up to 4 automated retry attempts that analyze the actual failure logs. At least overnight runs don't just accumulate broken PRs silently.
Also want to shout out Ouroboros (https://github.com/Q00/ouroboros), which comes at the problem from the opposite direction. Instead of better verification after generation, it uses Socratic questioning to score specification ambiguity before any code gets written. It literally won't let you proceed until ambiguity drops below a threshold. The core idea ("AI can build anything, the hard part is knowing what to build") pairs well with the verification-focused approaches everyone's discussing here. Spec refinement upstream, holdout validation downstream.
I don't think this is right becasue it's talking about Claude like it's a entity in the world. Claude reviewing Claude generated code and framing it like a individual reviewing it's own code isn't the same.
Adversarial AI code gen. Have another AI write the tests, tell Codex that Claude wrote some code and to audit the code and write some tests. Tell Gemini that Codex wrote the tests. Have it audit the tests. Tell Codex that Gemini thinks its code is bad and to do better. (Have Gemini write out why into dobetter.md)
You can always tell claude to use red-green-refactor and that really is a step-up from "yeah don't forget to write tests and make sure they pass" at the end of the prompt, sure. But even better, tell it to create subagents to form red team, green team and refactor team while the main instance coordinates them, respecting the clean-room rules. It really works.
The trick is just not mixing/sharing the context. Different instances of the same model do not recognize each other to be more compliant.
> But even better, tell it to create subagents to form red team, green team and refactor team while the main instance coordinates them, respecting the clean-room rules. It really works.
It helps, but it definitely doesn't always work, particularly as refactors go on and tests have to change. Useless tests start grow in count and important new things aren't tested or aren't tested well.
I've had both Opus 4.6 and Codex 5.3 recently tell me the other (or another instance) did a great job with test coverage and depth, only to find tests within that just asserted the test harness had been set up correctly and the functionality that had been in those tests get tested that it exists but its behavior now virtually untested.
Reward hacking is very real and hard to guard against.
The trick is, with the setup I mentioned, you change the rewards.
The concept is:
Red Team (Test Writers), write tests without seeing implementation. They define what the code should do based on specs/requirements only. Rewarded by test failures. A new test that passes immediately is suspicious as it means either the implementation already covers it (diminishing returns) or the test is tautological. Red's ideal outcome is a well-named test that fails, because that represents a gap between spec and implementation that didn't previously have a tripwire. Their proxy metric is "number of meaningful new failures introduced" and the barrier prevents them from writing tests pre-adapted to pass.
Green Team (Implementers), write implementation to pass tests without seeing the test code directly. They only see test results (pass/fail) and the spec. Rewarded by turning red tests green. Straightforward, but the barrier makes the reward structure honest. Without it, Green could satisfy the reward trivially by reading assertions and hard-coding. With it, Green has to actually close the gap between spec intent and code behavior, using error messages as noisy gradient signal rather than exact targets. Their reward is "tests that were failing now pass," and the only reliable strategy to get there is faithful implementation.
Refactor Team, improve code quality without changing behavior. They can see implementation but are constrained by tests passing. Rewarded by nothing changing (pretty unusual in this regard). Reward is that all tests stay green while code quality metrics improve. They're optimizing a secondary objective (readability, simplicity, modularity, etc.) under a hard constraint (behavioral equivalence). The spec barrier ensures they can't redefine "improvement" to include feature work. If you have any code quality tools, it makes sense to give the necessary skills to use them to this team.
It's worth being honest about the limits. The spec itself is a shared artifact visible to both Red and Green, so if the spec is vague, both agents might converge on the same wrong interpretation, and the tests will pass for the wrong reason. The Coordinator (your main claude/codex/whatever instance) mitigates this by watching for suspiciously easy green passes (just tell it) and probing the spec for ambiguity, but it's not a complete defense.
This is very interesting, but like sibling comments, I'm very curious as to how you run this in practice. Do you just tell Claude/Copilot to do what you describe?
And do you have any prompts to share?
How do you define visibility rules? Is that possible for subagents?
AFAIK Claude doesn't support it, but if you're willing to go the extra mile, you can get creative with some bash script: https://pastebin.com/raw/m9YQ8MyS (generated this a second ago - just to get the point across )
To be clear, I don't do this. I never saw an agent cheat by peeking or something. I really did look through their logs.
I'd be very interested to see claude code and other tools support this pattern when dispatching agents to be really sure.
> To be clear, I don't do this.
How do you know that it works then? Are you using a different tool that does support it?
So what do you do? Do you define roles somewhere and tell the agent to assign these roles to subagents?
> Reward hacking is very real and hard to guard against.
Is it really about rewards? Im genuinely curious. Because its not a RL model.
I'm noticing terms related to DL/RL/NLP are being used more and more informally as AI takes over more of the cultural zeitgeist and people want to use the fancy new terms of the era, even if inaccurately. A friend told me he "trained and fine tuned a custom agent" for his work when what he meant was he modified a claude.md file.
There is a nontrivial amount of RL training (RLHF, RLVR, ...), so it would be reasonable to call it an RL model.
And with that comes reward hacking - which isn't really about looking for more reward but rather that the model has learned patterns of behavior that got reward in the train env.
That is, any kind of vulnerability in the train env manifests as something you'd recognize as reward hacking in the real world: making tests pass _no matter what_ (because the train env rewarded that behavior), being wildly sycophantic (because the human evaluators rewarded that behavior), etc.
> Is it really about rewards? Im genuinely curious. Because its not a RL model.
Ha, good point. I was using it informally (you could handwave and call it an intrinsic reward if a model is well aligned to completing tasks as requested), but I hadn't really thought about it.
Searching around, it seems like I'm not alone, but it looks like "specification gaming" is also sometimes used, like: https://deepmind.google/blog/specification-gaming-the-flip-s...
They probably meant goal hacking. (I just made that up)
A refactor should not affect the tests at all should it? If it does, it's more than a refactor.
It can if your refactor needs to deal with interface changes, like moving methods around, changing argument order etc... all these need to propagate to the tests
Your tests are an assertion that 'no matter what this will never change'. If your interface can change then you are testing implementation details instead of the behavior users care about.
the above is really hard. A lot of tdd 'experts' don't understand is and teach fragile tests that are not worth having.
It depends on what you mean by "refactor" and how exactly you're testing, I guess, but that's not really at the heart of the point. red-green-refactor could also be used for adding new features, for instance, or an entire codebase, I guess.
I’m telling it to use red/green tdd [1] and it will write test that don’t fail and then says “ah the issue is already fixed” and then move on. You really have to watch it very closely. I’m having a huge problem with bad tests in my system despite a “governance model” that I always refer it to which requires red/green tdd.
[1] https://simonwillison.net/guides/agentic-engineering-pattern...
This sounds interesting. Can you go a bit deeper or provide references on how to implement the green/red/refactor subagent pattern?
What has worked better for me is splitting authority, not just prompts. One agent can touch app code, one can only write failing tests plus a short bug hypothesis, and one only reviews the diff and test output. Also make test files read only for the coding agent. That cuts out a surprising amount of self-grading behavior.
How do you limit access like that?
It’s not an agentic pattern, it’s an approach to test driven development.
You write a failing test for the new functionality that you’re going to add (which doesn’t exist yet, so the test is red). You then write the code until the test passes (that is, goes green).
That's the cool bit - you don't have to. CC is perfectly well aware and competent to implement it; just tell it to.
"So this is how liberty dies... with thunderous applause.” - Padmé Amidala
s/liberty/knowledge
Good idea, and an improvement, but you still have that fundamental issue: you don't really know what code has been written. You don't know the refactors are right, in alignment with existing patterns etc.
How exactly do you set up your CC sessions to do this?
thats a great idea - i have been using codex to do my code reviews since i have it to give better critique on code written by claude but havent tried it with testing yet!
codex/gpt is a stubborn model, doubt it would accept claude reviews or counter it. have seen cases where claude is more willing to comply if shared feedback though its just sycophancy too.
Am I supposed to be impressed by this? I think people are now just using agents for the sake of it. I'm perfectly happy running two simple agents, one for writing and one for reviewing. I don't need to go be writing code at faster than light speed. Just focusing on the spec, and watching the agent as it does its work and intervening when it goes sideways is perfectly fine with me. I'm doing 5-7x productivity easily, and don't need more than that.
I also spend most of my time reviewing the spec to make sure the design is right. Once I'm done, the coding agent can take 10 minutes or 30 minutes. I'm not really in that much of a rush.
I went the same way. At first I was splitting off work trees and running all the agents that I could afford, then I realized I just can't keep up with it all, running few agents around one issue in one directory is fast enough. Way faster than before and I can still follow what's happening.
> off work trees and running all the agents that I could afford,
I still think that we, programmers, having to pay money in order to write code is a travesti. And I'm not talking about paying the license for the odd text editor or even for an operating system, I'm talking about day-to-day operations. I'm surprised that there isn't a bigger push-back against this idea.
I would be impressed if I could say "here's $100 turn it into $1000" but you still gotta do the thinking.
I've been doing differential testing in Gemini CLI using sub-agents. The idea is:
1. one agent writes/updates code from the spec
2. one agent writes/updates tests from identified edge cases in the spec.
3. a QA agent runs the tests against the code. When a test fails, it examines the code and the test (the only agent that can see both) to determine blame, then gives feedback to the code and/or test writing agent on what it perceives the problem as so they can update their code.
(repeat 1 and/or 2 then 3 until all tests pass)
Since the code can never fix itself to directly pass the test and the test can never fix itself to accept the behavior of the code, you have some independence. The failure case is that the tests simply never pass, not that the test writer and code writer agents both have the same incorrect understanding of the spec (which is very improbable, like something that will happen before the heat death of the universe improbable, it is much more likely the spec isn't well grounded/ambiguous/contradictory or that the problem is too big for the LLM to handle and so the tests simply never wind up passing).
Where is the interface defined ? If it is just the coder reading the test it can hard code specific cases based on the test setup/fixture data.
I call this "Test Theatre" and it is real. I wrote about it last year:
https://benhouston3d.com/blog/the-rise-of-test-theater
You have to actively work against it.
This was really good, and second leaning on property testing. I’ve had really good outcomes from setting up Schemathesis and getting blanket coverage for stuff like “there should be no request you can generate as logged in user A that let’s you do things as or see things that belong to user B”, as well as “there should be no request you can find to any API endpoint that can trigger a 5xx response”
Test theatre is exactly the right framing. The tests are syntactically correct, they run, they pass but do they actually prove anything?
It's... really the same problem when you hire people to just write tests. A lot of time it just confirms that the code does what the code does. Having clear specs of what the code should do make things better and clearer.
Yep, tests written after the fact are just verifying tautologies.
> Most teams don't [write tests first] because thinking through what the code should do before writing it takes time they don't have.
It's astonishing to me how much our industry repeats the same mistakes over and over. This doesn't seem like what other engineering disciplines do. Or is this just me not knowing what it looks like behind the curtain of those fields?
That's probably dependent on your specific area of work. For most projects, It's okayish to deploy code with bugs. There will be future releases that fix those bugs and add improvements. Obviously that's not the case with high risk systems like space rockets software and similar.
With other engineering professions, all projects are like that. You cannot "deploy a bridge to production" to see what happens and fix it after a few have died
When push comes to shove, software can usually be fudged. Unlike a building or a water treatment plant where the first fuck up could mean that people die.
I like to think that people writing actual mission critical software try their absolute best to get it right before shipping and that the rest our industry exists in a totally separate world where a bug in the code is just actually not that big of a deal. Yeah, it might be expensive to fix, but usually it can be reverted or patched with only an inconvenience to the user and to the business.
It’s like the fines that multinational companies pay when breaking the law. If it’s a cost of doing business, it’s baked into the price of the product.
You see this also in other industries. OSHA violations on a residential construction site? I bet you can find a dozen if you really care to look. But 99% of the time, there are no consequences big enough for people to care so nobody wears their PPE because it “slows them down” or “makes them less nimble”. Sound familiar?
a lot of the value of tests is confirming that the system hasn't regressed beyond the behavior at the original release. It's bad if the original release is wrong, but a separate issue is if the system later accidentally stops behaving the way it did originally.
The issue I see is that the high test coverage created by having LLMs write tests results in almost all non-trivial changes breaking tests, even if they don't change behavior in ways that are visible from the outside. In one project I work, we require 100% test coverage, so people just have LLMs write tons of tests, and now every change I make to the code base always breaks tests.
So now people just ignore broken tests.
> Claude, please implement this feature.
> Claude, please fix the tests.
The only thing we've gained from this is that we can brag about test coverage.
Unit tests vs acceptance tests. You shouldn't be afraid to throw away unit tests if the implementation changes, and acceptance tests should verify behavior at API boundaries, ignoring implementation details.
I feel it end up a massive drag on development velocity and makes refactoring to simpler designs incredibly painful.
But hey, we're just supposed to let the AIs run wild and rewrite everything every change so maybe that's a heretic view.
yup agree - i think have specs and then do verifications against the spec. I have heard that this is how a lot of consulting firms work - you have acceptance criterias and thats how work is validated.
You can find approaches that improve things, but there's always going to be a chance that your code is terrible if you let an LLM generate it and don't review it with human eyes.
But review fatigue and resulting apathy is real. Devs should instead be informed if incorrect code for whatever feature or process they are working on would be high-risk to the business. Lower-risk processes can be LLM-reviewed and merged. Higher risk must be human-reviewed.
If the business you're supporting can't tolerate much incorrectness (at least until discovered), than guess what - you aren't going to get much speed increase from LLMs. I've written about and given conference talks on this over the past year. Teams can improve this problem at the requirements level: https://tonyalicea.dev/blog/entropy-tolerance-ai/
Sounds like we've just gotten into lazy mode where we believe that whatever it spits out is good enough. Or rather, we want to believe it, and convince ourselves that some simple guardrail we put up will make it true, because God forbid we have to use our own brain again.
What if instead, the goal of using agents was to increase quality while retaining velocity, rather than the current goal of increasing velocity while (trying to) retain quality? How can we make that world come to be? Because TBH that's the only agentic-oriented future that seems unlikely to end in disaster.
One thing I've been wrestling with building persistent agents is memory quality. Most frameworks treat memory as a vector store — everything goes in, nothing gets resolved. Over time the agent is recalling contradictory facts with equal confidence.
The architecture we landed on: ingest goes through a certainty scoring layer before storage. Contradictions get flagged rather than silently stacked. Memories that get recalled frequently get promoted; stale ones fade.
It's early but the difference in agent coherence over long sessions is noticeable. Happy to share more if anyone's going down this path.
Pet peeve: this post misunderstands “TDD.” What it really describes is acceptance tests.
TDD is a tool for working in small steps, so you get continuous feedback on your work as you go, and so you can refine your design based on how easy it is to use in practice. It’s “red green refactor repeat”, and each step is only a handful of lines of code.
TDD is not “write the tests, then write the code.” It’s “write the tests while writing the code, using the tests to help guide the process.”
Thank you for coming to my TED^H^H^H TDD talk.
> TDD is a tool for working in small steps, so you get continuous feedback on your work as you go, and so you can refine your design based on how easy it is to use in practice.
I would like to emphasize that feedback includes being alerted to breaking something you previously had working in a seemly unrelated/impossible way.
I guess to reach this point you have already decided you don't care what the code looks like.
Something I'm starting to struggle with is when agents can now do longer and more complex tasks, how do you review all the code?
Last week I did about 4 weeks of work over 2 days first with long running agents working against plans and checklists, then smaller task clean ups, bugfixes and refactors. But all this code needs to be reviewed by myself and members from my team. How do we do this properly? It's like 20k of line changes over 30-40 commits. There's no proper solution to this problem yet.
One solution is to start from scratch again, using this branch as a reference, to reimplement in smaller PRs. I'm not sure this would actually save time overall though.
I've been thinking a lot about this!
Redoing the work as smaller PRs might help with readability, but then you get the opposite problem: it becomes hard to hold all the PRs in your head at once and keep track of the overall purpose of the change (at least for me).
IMO the real solution is figuring out which subset of changes actually needs human review and focusing attention there. And even then, not necessarily through diffs. For larger agent-generated changes, more useful review artifacts may be things like design decisions or risky areas that were changed.
So you have become a reviewer instead of a programmer? Is that so? hones question. And if so, what is the advantage of looking a code for 12 hours instead of coding for 12.
It sounds like you know this but what happened is that you didn't do 4 weeks of work over 2 days, you got started on 4 weeks of work over 2 days, and now you have to finish all 4 weeks worth of work and that might take an indeterminate amount of time.
If you find a big problem in commit #20 of #40, you'll have to potentially redo the last 20 commits, which is a pain.
You seem to be gated on your review bandwidth and what you probably want to do is apply backpressure - stop generating new AI code if the code you previously generated hasn't gone through review yet, or limit yourself to say 3 PRs in review at any given time. Otherwise you're just wasting tokens on code that might get thrown out. After all, babysitting the agents is probably not 'free' for you either, even if it's easier than writing code by hand.
Of course if all this agent work is helping you identify problems and test out various designs, it's still valuable even if you end up not merging the code. But it sounds like that might not be the case?
Ideally you're still better off, you've reduced the amount of time being spent on the 'writing the PR' phase even if the 'reviewing the PR' phase is still slow.
yeah honestly thats what i am struggling with too and I dont have a a good solution. However, I do think we are going to see more of this - so it will be interesting to see how we are going to handle this.
i think we will need some kind of automated verification so humans are only reviewing the “intent” of the change. started building a claude skill for this (https://github.com/opslane/verify)
It's a nice idea, but how do you know the agent is aligned with what it thinks the intent is?
> how do you review all the code?
Code review is a skill, as is reading code. You're going to quickly learn to master it.
> It's like 20k of line changes over 30-40 commits.
You run it, in a debugger and step through every single line along your "happy paths". You're building a mental model of execution while you watch it work.
> One solution is to start from scratch again, using this branch as a reference, to reimplement in smaller PRs. I'm not sure this would actually save time overall though.
Not going to be a time saver, but next time you want to take nibbles and bites, and then merge the branches in (with the history). The hard lesson here is around task decomposition, in line documentation (cross referenced) and digestible chunks.
But if you get step debugging running and do the hard thing of getting through reading the code you will come out the other end of the (painful) process stronger and better resourced for the future.
Oh I didn't mean literally how do I review code. I meant, if an agent can write a lot of code to achieve a large task that seemingly works (from manual testing), what's the point if we haven't really solved code review? There's still that bottleneck no matter how fast you can get working code down.
>Last week I did about 4 weeks of work over 2 days first with long running agents working against plans and checklists, then smaller task clean ups, bugfixes and refactors. But all this code needs to be reviewed by myself and members from my team. How do we do this properly? It's like 20k of line changes over 30-40 commits. There's no proper solution to this problem yet.
Get an LLM to generate a list of things to check based on those plans (and pad that out yourself with anything important to you that the LLM didn't add), then have the agents check the codebase file by file for those things and report any mismatches to you. As well as some general checks like "find anything that looks incorrect/fragile/very messy/too inefficient". If any issues come up, ask the agents to fix them, then continue repeating this process until no more significant issues are reported. You can do the same for unit tests, asking the agents to make sure there are tests covering all the important things.
He admits the real hole himself: "this doesn't catch spec misunderstandings. If your spec was wrong to begin with, the checks will pass."
But there's a second problem underneath that one. Acceptance criteria are ephemeral. You write them before prompting, Playwright runs against them, and then where do they go? A Notion doc. A PR comment. Nowhere permanent. Next time an agent touches that feature, it's starting from zero again.
The commit that ships the feature should carry the criteria that verified it. Git already travels with the code. The reasoning behind it should too.
Did AI write this?
Nope - though I’ll take it as a compliment either way. It’s a problem I’ve been sitting with for a while, so the answer came out more formed than I expected. You disagree?
Our app is a desktop integration and last year we added a local API that could be hit to read and interact with the UI. This unlocked the same thing the author is talking about - the LLM can do real QA - but it's an example of how it can be done even in non-web environments.
Edit: I even have a skill called release-test that does manual QA for every bug we've ever had reported. It takes about 10 hours to run but I execute it inside a VM overnight so I don't care.
They're definitely inferior to proper tests, but even weak CC tests on top of CC code is an improvement over no tests. If CC does make a change that shifts something dramatically even a weak test may flag enough to get CC to investigate.
Even better though - external test suits. Recently made a S3 server of which the LLM made quick work for MVP. Then I found a Ceph S3 test suite that I could run against it and oh boy. Ended up working really good as TDD though.
yeah i have been hearing a lot more about this concept of “digital twins” - where you have high fidelity versions of external services to run tests against. You can ask the API docs of these external services and give it to Claude. Wonder if that is where we will be going more towards.
Isn’t this just an API sandbox? Many services have a test/sandbox mode. I do wish they were more common outside of fintech.
Wasn't the best practice to run one model/coding agent that writes the code and another one that reviews it? E.g. Claude Code for writing the code, GPT Codex to review/critique it? Different reward functions.
> When Claude writes tests for code Claude just wrote, it's checking its own work.
You can have Gemini write the tests and Claude write the code. And have Gemini do review of Claude's implementation as well. I routinely have ChatGPT, Claude and Gemini review each other's code. And having AI write unit tests has not been a problem in my experience.
yeah i have started using codex to do my code reviews and it helps to have “a different llm” - i think one of my challenges has been that unit tests are good but not always comprehensive. you still need functional tests to verify the spec itself.
I wish there was a way to "freeze" the tests. I want to write the tests first (or have Claude do it with my review), and then I want to get Claude to change the code to get them to pass - but with confidence that it doesn't edit any of the test files!
I use devcontainers in all the projects I use claude code on. [1] With it you can have claude running inside a container with just the project's code in write access and also mount a test folder with just read permissions, or do the opposite. You can even have both devcontainers and run them at the same time.
[1] https://code.claude.com/docs/en/devcontainer
If you want to try it just ask Claude to set it up for your project and review it after.
1. Make tests 2. Commit them 3. Proceed with implementation and tell agent to use the tests but not modify them
It will probably comply, and at least if it does change the tests you can always revert those files to where you committed them
Are there really no ways to control read/write permissions in a smart way? I've not had to do this yet, but is it really only capable of either being advisory with you implementing all the code, or it having full control over the repo where you just hope nothing important is changed?
You could probably make a system-level restriction so the software physically can't modify certain files, but I'm not sure how well that's going to fly if the program fails to edit it and there's no feedback of the failure.
You can use a Claude PreToolUse command hook to prevent write (or even read) access to specific files.
With this approach you can enforce that Claude cannot access to specific files. It’s a guarantee and will always work, unlike a prompt or Claude.md which is just a suggestion that can be forgotten or ignored.
This post has an example hook for blocking access to sensitive files:
https://aiorg.dev/blog/claude-code-hooks#:~:text=Protect%20s...
No. I don't want the mental burden of auditing whether it modified the tests.
Then, run the agent vm-sandboxed, with tests mounted as a read-only directory, if your directory structure allows it.
Or, less securely, hash the tests and check the hash with a hook, post tool use. Or a commit hook.
Why can't you do just that? You can configure file path permissions in Claude or via an external tool.
Why not use a client-server infrastructure for tests? The server sends the test code, the client runs the code, sends the output to the server and this replies pass/not pass.
One could even make zero-knowledge test development this way.
yeah i agree - this is somewhat the approach I have been using more of. Write the tests first based on specs and then write code to make the tests pass. This works well for cases where unit tests are sufficient.
You can remove edit permissions on the test directory
I'm not up to speed on Claude's features. Can I, from the prompt, quickly remove those permissions and then re-add them (i.e. one command to drop, and one command to re-add)?
Yeah, you can type `/permisssions` and do it there. Or you can make a custom slash command, or just ask Claude to do it. You can also set it when you launch a claude session, there are a dozen ways to do anything.
"Add a config option preventing you from modifying files matching src/*_test.py."
Just tell it that the tests can't be changed. Honestly I'd be surprised if it tried to anyway. I've never had it do that through many projects where tests were provided to drive development.
Anyone who wants a more programmatic version of this, check out cucumber / gherkin - very old school regex-to-code plain english kind of system.
> Changes land in branches I haven't read. A few weeks ago I realized I had no reliable way to know if any of it was correct: whether it actually does what I said it should do. I care about this. I don't want to push slop, and I had no real answer.
That’s really putting the cart before the horse. How do you get to “merging 50 PRs a week” before thinking “wait, does this do the right thing?”
Yeah just wanted to see what the bottlenecks would be as I started pushing the limits. Eventually made this into a verification skill(github.com/opslane/verify)
I am afraid that we are heading to a world in which we simply give up on the idea of correct code as an aspiration to strive for. Of course code has always been bad, and of course good code has never been a goal in the whole startup ecosystem (for perfectly legitimate reasons!). But that real production code, for services that millions or even billions of people rely on, should be reliable, that if it breaks that's a problem, this is the whole _engineering_ part of software engineering. And we can say: if we give that up we're going to have a whole lot more outages, security issues, all those things we are meant to minimize as a profession. And the answer is going to be: so what? We save money overall. And people will get used to software being unreliable; which is to say, people will not have a choice but to get used to it.
Feels like a whole bunch of us are converging on very similar patterns right now.
I've been building OctopusGarden (https://github.com/foundatron/octopusgarden), which is basically a dark software factory for autonomous code generation and validation. A lot of the techniques were inspired by StrongDM's production software factory (https://factory.strongdm.ai/). The autoissue.py script (https://github.com/foundatron/octopusgarden/blob/main/script...) does something really close to what others in this thread are describing with information barriers. It's a 6-phase pipeline (plan, review plan, implement, cold code review, fix findings, CI retry) where each phase only gets the context it actually needs. The code review phase sees only the diff. Not the issue, not the plan. Just the diff. That's not a prompt instruction, it's how the pipeline is wired. Complexity ratings from the review drive model selection too, so simple stuff stays on Sonnet and complex tasks get bumped to Opus.
On the test freezing discussion, OctopusGarden takes a different approach. Instead of locking test files, the system treats hand-written scenarios as a holdout set that the generating agent literally never sees. And rather than binary pass/fail (which is totally gameable, the specification gaming point elsewhere in this thread is spot on), an LLM judge scores satisfaction probabilistically, 0-100 per scenario step. The whole thing runs in an iterative loop: generate, build in Docker, execute, score, refine. When scores plateau there's a wonder/reflect recovery mechanism that diagnoses what's stuck and tries to break out of it.
The point about reviewing 20k lines of generated code is real. I don't have a perfect answer either, but the pipeline does diff truncation (caps at 100KB, picks the 10 largest changed files, truncates to 3k lines) and CI failures get up to 4 automated retry attempts that analyze the actual failure logs. At least overnight runs don't just accumulate broken PRs silently.
Also want to shout out Ouroboros (https://github.com/Q00/ouroboros), which comes at the problem from the opposite direction. Instead of better verification after generation, it uses Socratic questioning to score specification ambiguity before any code gets written. It literally won't let you proceed until ambiguity drops below a threshold. The core idea ("AI can build anything, the hard part is knowing what to build") pairs well with the verification-focused approaches everyone's discussing here. Spec refinement upstream, holdout validation downstream.
red / green / refactor is a reasonable way through this problem
I appear to be in the minority here. Perhaps because I've been practicing TDD for decades, this reads like the blog equivalent of "water is wet."
I don't think this is right becasue it's talking about Claude like it's a entity in the world. Claude reviewing Claude generated code and framing it like a individual reviewing it's own code isn't the same.
Adversarial AI code gen. Have another AI write the tests, tell Codex that Claude wrote some code and to audit the code and write some tests. Tell Gemini that Codex wrote the tests. Have it audit the tests. Tell Codex that Gemini thinks its code is bad and to do better. (Have Gemini write out why into dobetter.md)