The version of this I encounter literally every day is:
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.
Claude 4.7 broke something while we were working on several failing tests and justified itself like this:
> 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".
> Maybe we should just commit the signature change with a TODO
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).
Make it write a script with dry run and a file name list.
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.
Indeed you can! I don't use IntelliJ at work for [reasons], and LSP doesn't support a change signature action with defaults for new params (afaik). But it really seems like something any decent coding agent ought be able to one shot for precisely this reason, right?
That’s my daily experience too. There are a few more behaviours that really annoys me, like:
- it breaks my code, tests start to fail and it instantly says “these are all pre existing failures” and moves on like nothing happened
- or it wants to run some a command, I click the “nope” button and it just outputs “the user didn’t approve my command, I need to try again” and I need to click “nope” 10 more times or yell at it to stop
- and the absolute best is when instead of just editing 20 lines one after another it decides to use a script to save 3 nanoseconds, and it always results in some hot mess of botched edits that it then wants to revert by running git reset —hard and starting from zero. I’ve learned that it usually saves me time if I never let it run scripts.
The other day Codex on Mac gained the ability to control the UI. Will it close itself if instructed though? Maybe test that and make a benchmark. Closebench.
Indeed! You would think it would have some kind of sense that a commit that obviously won't compile is bad!
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.
Pretty sure it’s a harness or system prompt issue.
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.
On my compiled language projects I have a stop hook that compiles after every iteration. The agent literally cannot stop working until compilation succeeds.
In the case I described no code changes have been made yet. It's still just planning what to do.
It's true that I could accept the plan and hope that it will realize that it can't commit a change that doesn't compile on its own, later. I might even have some reason to think that's true, such as your stop hook, or a "memory" it wrote down before after I told it to never ever commit a change that doesn't compile, in all caps. But that doesn't change the badness of the plan.
Which is especially notable because I already told it the correct plan! It just tried to change the plan out of "laziness", I guess? Or maybe if you're enough of an LLM booster you can just say I didn't use exactly the right natural language specification of my original plan.
This has very little to do with someone making the LLM too human but rather a core limitation of the transformer architecture itself. Fundamentally, the model has no notion of what is normal and what is exceptional, its only window into reality is its training data and your added prompt. From the perspective of the model your prompt and its token vector is super small compared to the semantic vectors it has generated over the course of training on billions of data points.
How should it decide whether your prompt is actually interesting novel exploration of an unknown concept or just complete bogus? It can't and that is why it will fall back on output that is most likely (and therefore most likely average) with respect to its training data.
A very human thing to do is - not to tell us which model has failed like this! They are not all alike, some are, what I observe, order of magnitude better at this kind of stuff than others.
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.)
weird, for me it was too un-human at first, taking everything literally even if it doesn't make sense; I started being more precise with prompting, to the point where it felt like "metaprogramming in english"
claude on the other hand was exactly as described in the article
The thing is .. what else can you do? All the advice on how to get results out of LLMs talks in the same way, as if it's a negotiation or giving a set of instructions to a person.
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.
It's inherent in the way LLMs are built, from human-written texts, that they mimic humans. They have to. They're not solving problems from first principles.
> There was only one small issue: it was written in the programming language and with the library it had been told not to use. This was not hidden from it. It had been documented clearly, repeatedly, and in detail. What a human thing to do.
"Ignoring" instructions is not human thing. It's a bad LLM thing. Or just LLM thing.
The work where I've done well in my life (smashing deadlines, rescuing projects) has so often come because I've been willing to push back on - even explicitly stated - requirements. When clients have tried to replace me with a cheaper alternative (and failed) the main difference I notice is that the cheaper person is used to being told exactly what to do.
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".
It's not necessarily "ignoring" instructions, it's the ironic effect of mentioning something not to focus on, which produces focus on said thing. The classic version is: "For the next minute, try not to think about a pink elephant. You can think about anything else you like, just not a pink elephant."
Yes exactly. But for llms it's more that it's not really "thinking" about what it's saying per se, it's that it's predicting next token. Sure, in a super fancy way but still predicting next token. Context poisoning is real
The entire point of LLMs is that they produce statistically average results, so of course you're going to have problems getting them to produce non-average code.
Why, though? Just because some people would find it odd? Who cares?
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.
If you want to talk to the actual robot, the APIs seem to be the way to go. The prebuilt consumer facing products are insufferable by comparison.
"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.
I am quite hard anti-AI, but even I can tell what OP wants is a better library or API, NOT a better LLM.
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.
For agents I think the desire is less intrusive model fine-tuning and less opinionated “system instructions” please. Particularly in light of an agent/harness’s core motivation - to achieve its goal even if not exactly aligned with yours.
>Faced with an awkward task, they drift towards the familiar.
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.
I disagree. I wan't agents to feel at least a bit human-like. They should not be emotional, but I want to talk to it like I talk to a human. Claude 4.7 is already too socially awkward for me. It feels like the guy who does not listen to the end of the assignment, run to his desks, does the work (with great competence) only to find out that he missed half of the assignment or that this was only a discussion possible scenarios. I would like my coding agent to behave like a friendly, socially able and highly skilled coworker.
Interesting. When I code, I want a boring tool that just does the work. A hammer. I think we agree on that the tool should complete the assignment reliably, without skipping parts or turning an entirely implementable task into a discussion though.
>So no, I do not think we should try to make AI agents more human in this regard. I would prefer less eagerness to please, less improvisation around constraints, less narrative self-defence after the fact. More willingness to say: I cannot do this under the rules you set. More willingness to say: I broke the constraint because I optimised for an easier path. More obedience to the actual task, less social performance around it.
>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.
Interesting that what you're talking about as ASI is "as capable of handling explicit requirements as a human, but faster". Which _is_ better than a human, so fair play, but it's striking that this requirement is less about creativity than we would have thought.
I think the author is looking for something that doesn't exist (yet?). I don't think there's an agent in existence that can handle a list of 128 tasks exactly specified in one session. You need multiple sessions with clear context to get exact results. Ralph loops, Gastown, taskmaster etc are built for this, and they almost entirely exist to correct drift like this over a longer term. The agent-makers and models are slowly catching up to these tricks (or the shortcomings they exist to solve); some of what used to be standard practice in Ralph loops seems irrelevant now... and certainly the marketing for Opus 4.7 is "don't tell it what to do in detail, rather give it something broad".
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.
The article makes it seem like the author expected this without emptying context in between, which does not yet exist (actually I'm behind on playing with Opus 4.7, the Anthropic claim seems to be that longer sessions are ok now - would be interested to hear results from anyone who has).
That is probably the next step, and in practice it is much of what sub-agents already provide: a kind of tabula rasa. Context is not always an advantage. Sometimes it becomes the problem.
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.
I have the same experience of reversing intentional steps I've made, but with Claude Code. I find that committing a change that I want to version control seems to stop that behaviour.
Long context as disadvantage is pretty well discussed, and agent-native compaction has been inferior to having it intentionally build the documentation that I want it to use. So far this has been my LLM-coding superpower. There are also a few products whose entire purpose is to provide structure that overcomes compaction shortcomings.
When Geoff Huntley said that Claude Code's "Ralph loop" didn't meet his standards ("this aint it") the major bone of contention as far as I can see was that it ran subagents in a loop inside Claude Code with native compaction; as opposed to completely empty context.
I do see hints that improving compaction is a major area of work for agent-makers. I'm not certain where my advantage goes at that point.
Agreed. I am asking for something beyond the current state of the art. My guess is that stronger RL on the model side, together with better harness support, will eventually make it possible. However, it's the part about framing the failure to do complete a task as a communication mishap that really makes me go awry.
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.
> 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'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).
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.
It's true that I could accept the plan and hope that it will realize that it can't commit a change that doesn't compile on its own, later. I might even have some reason to think that's true, such as your stop hook, or a "memory" it wrote down before after I told it to never ever commit a change that doesn't compile, in all caps. But that doesn't change the badness of the plan.
Which is especially notable because I already told it the correct plan! It just tried to change the plan out of "laziness", I guess? Or maybe if you're enough of an LLM booster you can just say I didn't use exactly the right natural language specification of my original plan.
in any case, how is this specific to transformers?
It shouldn't. It should just do what it is told.
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
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.
"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".
https://en.wikipedia.org/wiki/Ironic_process_theory
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.
"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.
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.
Long context as disadvantage is pretty well discussed, and agent-native compaction has been inferior to having it intentionally build the documentation that I want it to use. So far this has been my LLM-coding superpower. There are also a few products whose entire purpose is to provide structure that overcomes compaction shortcomings.
When Geoff Huntley said that Claude Code's "Ralph loop" didn't meet his standards ("this aint it") the major bone of contention as far as I can see was that it ran subagents in a loop inside Claude Code with native compaction; as opposed to completely empty context.
I do see hints that improving compaction is a major area of work for agent-makers. I'm not certain where my advantage goes at that point.