I ask my coding agent to do some tedious, extremely well-specified refactor, such as (to give a concrete real life example) changing a commonly used fn to take a locale parameter, because it will soon need to be locale-aware. I am very clear — we are not actually changing any behavior, just the fn signature. In fact, at all call sites, I want it to specify a default locale, because we haven't actually localized anything yet!
Said agent, I know, will spend many minutes (and tokens) finding all the call sites, and then I will still have to either confirm each update or yolo and trust the compiler and tests and the agents ability to deal with their failures. I am ok with this, because while I could do this just fine with vim and my lsp, the LLM agent can do it in about the same amount of time, maybe even a little less, and it's a very straightforward change that's tedious for me, and I'd rather think about or do anything else and just check in occasionally to approve a change.
But my f'ing agent is all like, "I found 67 call sites. This is a pretty substantial change. Maybe we should just commit the signature change with a TODO to update all the call sites, what do you think?"
And in that moment I guess I know why some people say having an LLM is like having a junior engineer who never learns anything.
I have to ask, is this the sort of thing people use agents/AI for?
Because I'd probably reach for sed or awk.
Are they perfect? Far from it. But it's more comprehensive. Additionally simple refactors like this are insanely fast to review and so it's really easy to spot a bad change or etc. Plus i'm in Rust so it's typed very heavily.
In a lot of scenarios i'd prefer an AST grep over an LSP rename, but hat also doesn't cover the docs/comments/etc.
What a ridiculously backwards approach.
We were supposed to get agents who could use human tooling. Instead we are apparently told to write interfaces for this stumbling expensive mess to use.
Maybe, just maybe, if the human can know to, and use, the AST tool fine, the problem is not the tool but the agent.
> That's a behavior narrowing I introduced for simplicity. It isn't covered by the failing tests, so you wouldn't have noticed — but strictly speaking, [functionality] was working before and now isn't.
I know that a LLM can not understand its own internal state nor explain its own decisions accurately. And yet, I am still unsettled by that "you wouldn't have noticed".
I've been seeing more things like this lately. It's doing the weird kind of passive deflection that's very funny when in the abstract and very frustrating when it happens to you.
I see this kind of misalignment in all agents, open and closed weights.
I've found these forms to be the most common, "this test was already failing before my changes." Or, "this test is flaky due to running the test suite on multiple threads." Sometimes the agent cot claims the test was bad, or that the requirements were not necessary.
Even more interesting is a different class of misalignment. When the constraints are very heavy (usually towards the end of the entire task), I've observed agents intentionally trying to subvert the external validation mechanisms. For example, the agent will navigate out of the work tree and commit its changes directly to trunk. They cot usually indicates that the agent "is aware" that it's doing a bad think. This usually is accompanied by something like, "I know that this will break the build, but I've been working on this task for too long, I'll just check what I have in now and create a ticket to fix the build."
I ended up having to spawn the agents in a jail to prevent that behavior entirely.
I think some of this is a problem in the agent's design. I've got a custom harness around GPT5.4 and I don't let my agent do any tool calling on the user's conversation. The root conversation acts as a gatekeeper and fairly reliably pushes crap responses like this back down into the stack with "Ok great! Start working on items 1-20", etc.
I'm fascinated that so many folks report this, I've literally never seen it in daily CC use. I can only guess that my habitually starting a new session and getting it to plan-document before action ("make a file listing all call sites"; "look at refactoring.md and implement") makes it clear when it's time for exploration vs when it's time for action (i.e. when exploring and not acting would be failing).
The real deal is, while a Semi can do all the things you can do with a scooter, the opposite is not true.
You may be able to lane split in a semi, but it also has excessive environmental impact.
You’ll be amazed how good the script is.
My agent did 20 class renames and 12 tables. Over 250 files and from prompt to auditing the script to dry run to apply, a total wall clock time of 7 minutes.
Took a day to review but it was all perfect!
Reminds us of the most important button the "AI" has, over the similarly bad human employee.
'X'
Until, of course, we pass resposibility for that button to an "AI".
You would think.
It would be one thing if it was like, ok, we'll temporarily commit the signature change, do some related thing, then come back and fix all the call sites, and squash before merging. But that is not the proposal. The plan it proposes is literally to make what it has identified as the minimal change, which obviously breaks the build, and call it a day, presuming that either I or a future session will do the obvious next step it is trying to beg off.
I have never seen those “minimal change” issues when using zed, but have seen them in claude code and aider. Been using sonnet/opus high thinking with the api in all the agents I have tested/used.
For example, it does a bunch of stuff, and I look at it and I say, "Did we already decide to do [different approach]" And then it runs around and says, "Oh yeah," and then it does a thousand more steps and undoes does what it just did and gets itself into a tangle.
Meanwhile, I asked it a question. The proper response would be to answer the question. I just want to know the answer.
I had it right. That behavior into a core memory, and it seems to have improved for what it's worth.
It has almost everything to do with it. Models have been fine-tuned to generate outputs that humans prefer.
in any case, how is this specific to transformers?
It shouldn't. It should just do what it is told.
You can do a mental or physical search and replace all references to the LLM as "it" if you like, but that doesn't change the interaction.
> I asked an AI agent to solve a programming problem
You're not asking it to solve anything. You provide a prompt and it does autocomplete. The only reason it doesn't run forever is that one of the generated tokens is interpreted as 'done'.
With the same reasoning, human being are only a bunch of atoms, and the only reason they don't collide with other humans is because of the atomic force.
When your abstraction level is too low, it doesn't explain anything, because the system that is built on it is way too complex.
They'll probably get really good at model approximation, as there's a clear reward signal, but in places where that feedback loop is not possible/very difficult then we shouldn't expect them to do well.
"Ignoring" instructions is not human thing. It's a bad LLM thing. Or just LLM thing.
Maybe this is more anthropomorphising but I think this pushing back is exactly the result that the LLMs are giving; but we're expecting a bit too much of them in terms of follow-up like: "ok I double checked and I really am being paid to do things the hard way".
I believe how "neurotypical" (for the lack of a better word) you want model to be is a design choice. (But I also believe model traits such as sycophancy, some hallucinations or moral transgressions can be a side effect of training to be subservient. With humans it is similar, they tend to do these things when they are forced to perform.)
EDIT: It's specifically GPT-5.4 High in the Codex harness.
claude on the other hand was exactly as described in the article
Trying to limit / disallow something seems to be hurting the overall accuracy of models. And it makes sense if you think about it. Most of our long-horizon content is in the form of novels and above. If you're trying to clamp the machine to machine speak you'll lose all those learnings. Hero starts with a problem, hero works the problem, hero reaches an impasse, hero makes a choice, hero gets the princess. That can be (and probably is) useful.
LLM write in the first person because they have been specifically been finetuned for a chat task, it's not a fundamental feature of language models that would have to be specifically disallowed
"ChatGPT wrapper" is no longer a pejorative reference in my lexicon. How you expose the model to your specific problem space is everything. The code should look trivial because it is. That's what makes it so goddamn compelling.
Once again, one of the things I blame this moment for is people are essentially thinking they can stop thinking about code because the theft matrices seem magical. What we still need is better tools, not replacements for human junior engineers.
They drift to their training data. If thousand of humans solved a thing in a particular way, it's natural that AI does it too, because that is what it knows.
We obviously have different expectations for the behavior of coding agent,s sp options to set the social behavior will become important.
More of that please. Perhaps on a check box, "[x] Less bullsh*t".
>Less human AI agents, please.
Agents aren't humans. The choices they make do depend on their training data. Most people using AI for coding know that AI will sometime not respect rules and the longer the task is, the more AI will drift from instructions.
There are ways to work around this: using smaller contexts, feeding it smaller tasks, using a good harness, using tests etc.
But at the end of the day, AI agents will shine only if they are asked to to what they know best. And if you want to extract the maximum benefit from AI coding agents, you have to keep that in mind.
When using AI agents for C# LOB apps, they mostly one shot everything. Same for JS frontends. When using AI to write some web backends in Go, the results were still good. But when I tried asking to write a simple cli tool in Zig, it pretty much struggled. It made lots of errors, it was hard to solve the errors. It was hard to fix the code so the tests pass. Had I chose Python, JS, C, C#, Java, the agent would have finished 20x faster.
So, if you keep in mind what the agent was trained on, if you use a good harness, if you have good tests, if you divide the work in small and independent tasks and if the current task is not something very new and special, you are golden.
In fairness to coding agents, most of coding is not exactly specified like this, and the right answer is very frequently to find the easiest path that the person asking might not have thought about; sometimes even in direct contradiction of specific points listed. Human requirements are usually much more fuzzy. It's unusual that the person asking would have such a clear/definite requirement that they've thought about very clearly.
Just as a human would use a task list app or a notepad to keep track of which tasks need to be done so can a model.
You can even have a mechanism for it to look at each task with a "clear head" (empty context) with the ability to "remember" previous task execution (via embedding the reasoning/output) in case parts were useful.
In long editing sessions with multiple iterations, the context can accumulate stale information, and that actively hurts model performance. Compaction is one way to deal with that. It strips out material that should be re-read from disk instead of being carried forward.
A concrete example is iterative file editing with Codex. I rewrite parts of a file so they actually work and match the project’s style. Then Codex changes the code back to the version still sitting in its context. It does not stop to consider that, if an external edit was made, that edit is probably important.
