"Agents address the problem from independent angles, other agents try to refute what they found, and the run keeps iterating until the answers converge."
So you will be supplying the "ground truth" (test suite, detailed spec, whatever) and empower an agent to use it to guide the other agents. Currently a lot of people do this sequentially in the form of multiple code-review passes by fresh agent sessions looking at the work of previous sessions.
Adversarial models are a longstanding technique in ML so it makes sense they would try to go this way.
I don't know, maybe I'm doing it wrong but I feel LLMs add a slop debt, and each agent pass just exuberates it.
Like I had an LLM implement a spec and said it was done... Except it had a ton of `casts` everywhere. Okay, my bad, I should have been clear "NO CASTS", so I use the LLM to remove the casts, except it just kept making things more and more complicated and ugly.
It took me taking a break and having a shower thought to realize all the ugliness is because one type should have been broken up into 2, which would remove a ton of generics and code. But Claude never suggested that, it was always "we need at least one cast here, or we need 1000 LOC of generic factories". I tried multiple new sessions with various prompts too.
Maybe one day soon LLMs could pay off their own slop debt but at least right now I don't trust them to write code unseen.
Edit: Maybe the correct action should have been to delete everything and make it re-write everything from scratch with the clear "NO CASTS EVER" rule. But still the point is feels like having LLM clean up after an LLM doesn't work well enough to just have keep it in a loop and never look at what it does.
I've had to put a fair chunk of effort in to skills that will run deterministic mechanisms to unslop a codebase (cyclomatic complexity grading has been really helpful here) as invariably some amount of guidance around principles will be missed over time. I've found it does help, though. Certainly I'm getting overall better results from Flash and Sonnet over multiple runs for fairly modest token increases. GPT 5.5 less so, but that's because it scores better in a first pass. I won't really know until I gauge it at the end of my sub month which has been more cost efficient for me all things considered.
The problem is that we have an ever growing and large number of constraints, and not following even a single one means the result is sloppy.
I don’t see them fixing this any time soon, and thus human in the loop is a requirement to use these tools effectively. That is unless you love your slot machine dopamine rush enough to ignore quality gates and respect for your peers time.
I’m in a similar boat. I find that longer sessions will introduce “noise”. I have to be extremely explicit to avoid adding this noise, as it pollutes the future output of the models. Sometimes it’s innocuous, other times it can derail sessions as the 2nd or 3rd pass introduce even more of their own noise.
To me, it seems the models are inherently designed to do this. Creating more verbose output than input, generating plans introduce things I didn’t ask for, extras, more “defensive” code that makes sense at first but is completely unnecessary in practice… I find it exhausting, but it’s important to pare down the output / plans at each stage and trim the generated stuff that isn’t needed.
I've been reading writing Rust for a long while now, since before 1.0. I'm capable of critically evaluating Rust code. I'm also a happy Claude Code user, mostly for lightweight uses like generating scaffolding, prototyping, and debugging.
The pure LLM, no human intervention vibe-coded PRs on Bun since the vibe-rewrite to Rust contain the worst coding horrors I've seen in 20 years of programming.
Setting aside the quality of the change itself (I would have done it differently, for sure: it is pretty straightforward to build a safe abstraction out of this type), the utterly pointless "source-text consistency test" added here is easily the worst example of "test repeats implementation" I have seen in my career:
Write a skill outlining your expectations of the code, put that skill into the pipeline, so that it can be included within your workflow.
Webdev here, but currently I have:
- a skill where I outlined how the architecture of the system should look like, with guards (static analysis, architecture tests, linting) confirming that the code it generates adheres to standards
- a skill that tells it how tests should look like (use generators, write both feature / unit tests)
- a skill that tells it to generate docs from the code in a form of acceptance criteria (Given / When / Then)
- a skill that tells it to generate frontend uat tests + accompanying backend seeders given the AC
- a skill that tells it to verify that ticket objectives match what was delivered
At this point I still need to guide it to move task from one stage to the other (coding, testing, verification that indeed what was coded adheres to what was required), but I believe that these dynamic workflows can automate this work as well.
How would you prevent a junior engineer doing this mistake? Presumably, you would setup a lint rule. Do the same for LLMs. Run the linter after each edit through a hook, give feedback to the LLM. Write your lint rules with clear explanations of why the behavior is a problem, and nudges to the good behavior.
You wouldn't prevent the junior from making this mistake.
You would correct them once or twice, and they won't make the mistake again.
It's something we can't do with LLM's currently, so we all just try(and fail) to predict any possible failure ever, and then somehow try to cram it into the limited context.
> Currently a lot of people do this sequentially in the form of multiple code-review passes by fresh agent sessions looking at the work of previous sessions.
Up until now I've used a review loop approach, where within a Claude Code session I just tell it to spawn three review sub-agents, each with context of what's going on and instructions to look over all of the changed code in search for serious/critical issues, but otherwise a more fresh look at things. It works really well for the most part (token usage aside): https://news.ycombinator.com/item?id=48277011
This reminds me of something I do manually all the time (tell Claude to do something, ask another instance what the first one did wrong, etc), and it has given me good results. If the harness can now automate that, it seems great!
So, theoretically, in order to get claude to perform properly you just have to have a perfect spec up front, that anticipates all issues and contains no ambiguity?
Ground truth is not consensus, it has to be graded against what actually works for the original goal. Plenty of scenarios with AI and Humans can result in consensus around incorrectness.
While pedantically correct, I think the comment above assumed that you've correctly specified the work. If you can't correctly specify your work, AI agents are just going to help you get a non-solution faster.
Isn't coding the act of specificying the work to a processor? And AI agents are supposed to bridge the gap with intelligence from less specificed to more specified or possibly even more intelligent and alternate implementations?
What I meant by "ground truth" is that it is not fuzzy, not AI-evaluated, and not a consensus. The test suite passes or it doesn't. The codebase lints or it doesn't. The performance improved or it didn't.
An agent can help you create the specification, but it's up to you to know whether it's correctly testing that you got the result you wanted.
"Agents address the problem from independent angles, other agents try to refute what they found, and the run keeps iterating until the answers converge."
So you will be supplying the "ground truth" (test suite, detailed spec, whatever) and empower an agent to use it to guide the other agents. Currently a lot of people do this sequentially in the form of multiple code-review passes by fresh agent sessions looking at the work of previous sessions.
Adversarial models are a longstanding technique in ML so it makes sense they would try to go this way.