Comment by pierrekin
20 hours ago
There is something darkly comical about using an LLM to write up your “a coding agent deleted our production database” Twitter post.
On another note, I consider users asking a coding agent “why did you do that” to be illustrating a misunderstanding in the users mind about how the agent works. It doesn’t decide to do something and then do it, it just outputs text. Then again, anthropic has made so many changes that make it harder to see the context and thinking steps, maybe this is an attempt at clawing back that visibility.
If you ask humans to explain why we did something, Sperry's split brain experiment gives reason to think you can't trust our accounts of why we did something either (his experiments showed the brain making up justifications for decisions it never made)
Bit it can still be useful, as long as you interpret it as "which stimuli most likely triggered the behaviour?" You can't trust it uncritically, but models do sometimes pinpoint useful things about how they were prompted.
Humans can do one thing that AI agents are 100% completely incapable of doing: being accountable for their actions.
You haven't met certain humans. Not all humans have internal capacity for accountability.
The real meaning of accountability is that you can fire one if you don't like how they work. Good news! You can fire an AI too.
9 replies →
Don’t forget learning, humans can learn, LLMs do not learn, they are trained before use.
4 replies →
What does that actually mean in practice? You can yell at human if it makes you feel better, sure, but you can do that with an AI agent too, and it's approximately as productive.
I disagree. They could fire Claude and their legal counsel could pursue claims (if there were any, idk)-- the accountability model is similar. Anthropic probably promised no particular outcome, but then what employee does?
And in the reverse, if a person makes a series of impulsive, damaging decisions, they probably will not be able to accurately explain why they did it, because neither the brain nor physiology are tuned to permit it.
Seems pretty much the same to me.
1 reply →
That’s a feature that other humans impose on whoever’s being held accountable. There’s no reason in principle we couldn’t do the same with agents.
1 reply →
Yep.
You might as well be asking a tape recorder why it said something. Why are we confusing the situation with non-nonsensical comparisons?
There is no internal monologue with which to have introspection (beyond what the AI companies choose to hide as a matter of UX or what have you). There is no "I was feeling upset when I said/did that" unless it's in the context.
There is no ghost in the machine that we cannot see before asking.
Even if a model is able to come up with a narrative, it's simply that. Looking at the log and telling you a story.
Sperry's experiments makes it quite clear that the comparison is not nonsensical: humans can't reliably tell why we do things either. It is not imbuing AI with anything more to recognise that. Rather pointing out that when we seek to imply the gap is so huge we often overestimate our own abilities.
7 replies →
I think you might be misinterpreting that. I always understood it to mean that when the two hemispheres can't communicate, they'll make things up about their unknowable motivations to basically keep consciousness in a sane state (avoiding a kernel panic?). I don't think it's clear that this happens when both hemispheres are able to communicate properly. At least, I don't think you can imply that this special case is applicable all the time.
We have no reason to believe it is a special case. The fact that these patients largely functioned normally when you did not create a situation preventing the hemispheres from synchronising suggests otherwise to me. There's no reason to think the ability to just make things up and treat it as if it is truthful recollection would just disappear because there are two halves that can lie instead of just one.
None of the developers that I’ve worked with have had the hemispheres of their brains severed. I suspect this is pretty rare in the field.
> None of the developers that I’ve worked with have had the hemispheres of their brains severed.
But are their explanations for how they behaved any more compelling than those of people who have? If so, why?
This still doesnt stop post ad hoc explanations by humans.
2 replies →
The thing is, the LLM mostly just states what it did, and doesn't really explain it (other than "I didn't understand what I was doing before doing it. I didn't read Railway's docs on volume behavior across environments."). Humans are able of more introspection, and usually have more awareness of what leads them to do (or fail to do) things.
LLMs are lacking layers of awareness that humans have. I wonder if achieving comparable awareness in LLMs would require significantly more compute, and/or would significantly slow them down.
Sperry's experiments suggests we don't have that awareness, but think we do as our brains will make up an explanation on the spot.
I agree that the model can help troubleshoot and debug itself.
I argue that the model has no access to its thoughts at the time.
Split brain experiments notwithstanding I believe that I can remember what my faulty assumptions were when I did something.
If you ask a model “why did you do that” it is literally not the same “brain instance” anymore and it can only create reasons retroactively based on whatever context it recorded (chain of thought for example).
Anthropic's introspection experiments have seemed to show that your argument is falsifiable.
https://www.anthropic.com/research/introspection
4 replies →
Claude code and codex both hide the Chain of Thought (CoT) but it's just words inside a set of <thinking> tags </thinking> and the agent within the same session has access to that plaintext.
8 replies →
It does have access to its thoughts. This is literally what thinking models do. They write out thoughts to a scratch pad (which you can see!) and use that as part of the prompt.
7 replies →
That is absolutely not what the split brain experiment reveals. Why would you take results received from observing the behavior of a highly damaged brain, and use them to predict the behavior of a healthy brain? Stop spreading misinformation.
Such 'highly damaged' brain is still 90 percent or more structured the same as a normal human brain. See it as a brain that runs in debug mode.
It is known that the narrative part of the brain is separate from the decision taking brain. If someone asks you, in a very convincing, persuasive way, why you did something a year ago and you can't clearly remember you did, it can happen that you become positive that you did so anyway. And then the mind just hallucinates a reason. That's a trait of brains.
1 reply →
Because said "highly damaged brain" in most respects still functions pretty much like a healthy one.
There is no misinformation in what I wrote.
> a misunderstanding in the users mind about how the agent work
On top of that the agent is just doing what the LLM says to do, but somehow Opus is not brought up except as a parenthetical in this post. Sure, Cursor markets safety when they can't provide it but the model was the one that issued the tool call. If people like this think that their data will be safe if they just use the right agent with access to the same things they're in for a rude awakening.
From the article, apparently an instruction:
> "NEVER FUCKING GUESS!"
Guessing is literally the entire point, just guess tokens in sequence and something resembling coherent thought comes out.
Good point, it's like having an instruction "Never fucking output a token just because it's the one most likely to occur next!!1!"
That is actually pretty good, LLM's gonna LLM
Twitter users get paid for these 'articles' based on engagement, correct? That may be the reason why it is so dramatized.
It's one way for the company to make its money back, I guess.
Naw, we just want people to know. We followed all Cursor rules, thought we had protected all API keys, and trusted the backups of a heavily used infrastructure company. Cautionary tale sharing with others.
1 reply →
Yes, you're right, in that there's no decision module separate from the output. It overcommits in the other direction.
The post-hoc reasoning the model produces when you ask "why did you do that" is also just text, and yet that text often matches independent third-party analysis of the same behavior at well above chance. If it really were uncorrelated text-completion, the post-hoc explanation should not align with the actual causes more than randomly. It does, frequently enough that I've stopped using it as evidence the user is naive.
"just outputs text" is doing more work than we acknowledge. The person asking the agent "why did you do that" might be an idiot for expecting anything more than a post-hoc rationalization, but that's exactly what you'd expect from a human too.
> There is something darkly comical about using an LLM to write up
It feels like a modern greek tragedy. Man discovers LLMs are untrustworthy, then immediately uses an LLM as his mouthpiece.
Delicious!
> There is something darkly comical about using an LLM to write up your “a coding agent deleted our production database” Twitter post.
Which calls into question if this is even real.
While I largely agree, it does raise the prospect of testing this iteratively. E.g., give a model some fake environment, prompt it random things until it does something "bad" in your fake environment, and then fix whatever it claims led to its taking that action.
If you can do this and reliably reduce the rate at which it does bad things, then you could reasonably claim that it is aware of meaningful introspection.
> systemic failures across two heavily-marketed vendors that made this not only possible but inevitable.
> No confirmation step. No "type DELETE to confirm." No "this volume contains production data, are you sure?" No environment scoping. Nothing.
> The agent that made this call was Cursor running Anthropic's Claude Opus 4.6 — the flagship model. The most capable model in the industry. The most expensive tier. Not Composer, not Cursor's small/fast variant, not a cost-optimized auto-routed model. The flagship.
The tropes, the tropes!!
https://tropes.fyi/
So if tropes.md works it doesn’t actually solve the problem. You’ll be reading stuff that you think an LLM didn’t write.
Beyond that, isn't it just going to make up a narrative to fit what's in the prompt and context?
I don't think there's any special introspection that can be done even from a mechanical sense, is there? That is to say, asking any other model or a human to read what was done and explain why would give you just an accounting that is just as fictional.
Not necessarily. The people saying that in this thread seem to be forgetting about the encrypted reasoning tokens. The why of a decision is often recorded in a part of the context window you can't see with modern models. If you ask a model, "why did you do that" it isn't necessarily going to make up a plausible answer - it can see the reasoning traces that led up to that decision and just summarize them.
Seems like they’ve already reached the point where they’ve forgotten how to think.
An LLM will reply with a plausible explanation of why someone would have written the code that it just wrote. Seems about the same.
Not some vibe coder, and AI agents can be incredibly powerful. But yes, the irony is not lost on us!
Is there a reason you weren’t able to write the post yourself?
Vibe coder doesn't realize or denying he is a vibe coder, what other reason did you want
> It doesn’t decide to do something and then do it, it just outputs text.
We can debate philosophy and theory of mind (I’d rather not) but any reasonable coding agent totally DOES consider what it’s going to do before acting. Reasoning. Chain of thought. You can hide behind “it’s just autoregressively predicting the next token, not thinking” and pretend none of the intuition we have for human behavior apply to LLMs, but it’s self-limiting to do so. Many many of their behaviors mimic human behavior and the same mechanisms for controlling this kind of decision making apply to both humans and AI.
I suspect we are not describing the same thing.
When a human asks another human “why did you do X?”, the other human can of course attempt to recall the literal thoughts they had while they did X (which I would agree with you are quite analogous to the LLMs chain of thought).
But they can do something beyond that, which is to reason about why they may have the beliefs that they had.
“Why did you run that command?”
“Because I thought that the API key did not have access to the production system.”
When a human responds with this they are introspecting their own mind and trying to project into words the difference in understanding they had before and after.
Whereas for an agent it will happily include details that are not literally in its chain of thought as justifications for its decisions.
In this case, I would argue that it’s not actually doing the same thing humans do, it is creating a new plausible reason why an agent might do the thing that it itself did, but it no longer has access to its own internal “thought state” beyond what was recorded in the chain of thought.
> Whereas for an agent it will happily include details that are not literally in its chain of thought as justifications for its decisions.
Humans do this too, ALL THE TIME. We rationalize decisions after we make them, and truly believe that is why we made the decision. We do it for all sorts of reasons, from protecting our ego to simply needing to fill in gaps in our memory.
Honestly, I feel like asking an AI it’s train of thought for a decision is slightly more useful than asking a human (although not much more useful), since an LLM has a better ability to recreate a decision process than a human does (an LLM can choose to perfectly forget new information to recreate a previous decision).
Of course, I don’t think it is super useful for either humans or LLMs. Trying to get the human OR LLM to simply “think better next time” isn’t going to work. You need actual process changes.
This was a rule we always had at my company for any after incident learning reviews: Plan for a world where we are just as stupid tomorrow as we are today. In other words, the action item can’t be “be more careful next time”, because humans forget sometimes (just like LLMs). You will THINK you are being careful, but a detail slips your mind, or you misremember what situation you are in, or you didn’t realize the outside situation changed (e.g. you don’t realize you bumped the keyboard and now you are typing in another console window).
Instead, the safety improvements have to be about guardrails you put up, or mitigations you put in place to prevent disaster the NEXT time you fail to be as careful as you are trying to be.
Because there is always a next time.
Honestly, I think the biggest struggle we are having with LLMs is not knowing when to treat it like a normal computer program and when to treat it like a more human-like intelligence. We run across both issues all the time. We expect it to behave like a human when it doesn’t and then turn around and expect it to behave like a normal computer program when it doesn’t.
This is BRAND NEW territory, and we are going to make so many mistakes while we try to figure it out. We have to expect that if you want to use LLMs for useful things.
4 replies →
I agree with you a LLM is perfectly capable of explaining its actions.
However it cannot do so after the fact. If there's a reasoning trace it could extract a justification from it. But if there isn't, or if the reasoning trace makes no sense, then the LLM will just lie and make up reasons that sound about right.
So it is equal to what neuroscientists and psychologists have proven about human beings!
2 replies →
> asking a coding agent “why did you do that” to be illustrating a misunderstanding in the users mind about how the agent works
I think the same thing, but about agents in general. I am not saying that we humans are automata, but most of the time explanation diverges profoundly from motivation, since motivation is what generated our actions, while explanation is the process of observing our actions and giving ourselves, and others around us, plausible mechanics for what generated them.