> I queued the work and let it run. First task came back good. Second came back good. Somewhere around hour four the quality started sliding. By hour six the agent was cutting corners I’d specifically told it not to cut, skipping steps I’d explicitly listed, behaving like I’d never written any of the rules down.
> …
> When I write a prompt, the agent doesn’t just read the words. It reads the shape. A short casual question gets read as casual. A long precise document with numbered rules gets read as… not just the rules, but also as a signal. “The user felt the need to write this much.” “Why?” “What’s going on here?” “What do they really want?”
This is an interesting premise but based on the information supplied, I don’t think it’s the only conclusion. Yet the whole essay seems to assume it is true and then builds its arguments on top of it.
I’ve run into this dilemma before. It happens when there’s a TON of information in the context. LLMs start to lose their attention to all the details when there’s a lot of it (e.g. context rot[0]). LLMs also keep making the same mistakes once the information is in the prompt, regardless of attempts to convey it is undesired[1]
I think these issues are just as viable to explain what the author was facing. Unless this is happening with much less information
If you ask a vague ignorant question, you get back authoritative summaries. If you make specific request, each statement is taken literally. The quality of the answer depends on the quality of the question.
And I'm not using "quality" to mean good/bad. I mean literally qualitative, not quantifiable. Tone. Affect. Personality. Whatever you call it. Your input tokens shape the pattern of the output tokens. It's a model of human language, is that really so surprising?
To me, after the novelty of seeing a computer program execute (more or less) what I ask in plain English wears off, what’s left is the chore of managing a bunch of annoying bots.
I don’t know yet if we’re more productive or not, if the resulting code is as good. But the craft in itself is completely different, much more akin to product managing, psychology, which I never enjoyed as much.
> I can imagine a future in which some or even most software is developed by witches, who construct elaborate summoning environments, repeat special incantations (“ALWAYS run the tests!”), and invoke LLM daemons who write software on their behalf. These daemons may be fickle, sometimes destroying one’s computer or introducing security bugs, but the witches may develop an entire body of folk knowledge around prompting them effectively—the fabled “prompt engineering”. Skills files are spellbooks.
Great article, best insight into autistic<->neurotypical communication styles.
Couldn’t you have a “communications” LLM massage your prompts to the “main” LLM so that it removes the queues that cause the main LLM to mistakenly infer your state of mind?
The fundamental idea is that "intelligence" really means trying to shorten the time to figure out something. So it's a tradeoff, not a quality. And AI agents are doing it.
Therefore, if that perspective is right, the issues that the OP describes are inherent to intelligent agents. They will try to find shortcuts, because that's what they do, it's what makes them intelligent in the first place.
People with ASD or ADHD or OCD, they are idiot-savants in the sense of that paper. They insist on search for solutions which are not easy to find, despite the common sense (aka intelligence) telling them otherwise.
It's a paradox that it is valuable to do this, but it is not smart. And it's probably why CEOs beat geniuses in the real world.
CEOs beat geniuses in the real world because they often have other pathologies, like enough moral flexibility to ignore the externalities of their profit centers.
I'd also argue there's some training bias in the performance, it's not just smart shortcuts... Claude especially seems prone to getting into a 'wrap it up' mode even when the plan is only half way completed and starts deferring rather than completing tasks.
> The fundamental idea is that "intelligence" really means trying to shorten the time to figure out something.
"Figure out" implies awareness and structured understanding. If we relax the definition too much, then puddles of water are intelligent and uncountable monkeys on typewriters are figuring out Shakespeare.
Is there a name for this style of writing? Where it's composed exclusively of simple sentences. Short and punchy.
Paragraphs with just a single sentence.
I know it's associated with LLM writing. This article probably wasn't written by an LLM. But still. It has a kind of rhythm to it. Like poetry. But poetry designed to put me to sleep.
Yes, this was super annoying to read. It was some core ideas and it was expanded into a way too long essay that boiled down to this guy doesn't know how to run agents.
This is very well written and told. It’s worth reading all the way through.
> If you try to refute it, you’ll just get another confabulation.
> Not because the model is lying to you on purpose, and not because it’s “resistant” or “defensive” in the way a human might be. It’s because the explanation isn’t connected to anything that could be refuted. There is no underlying mental state that generated “I sensed pressure.” There is a token stream that was produced under a reward function that prefers human-sounding, emotionally framed explanations. If you push back, the token stream that gets produced next will be another human-sounding, emotionally framed explanation, shaped by whatever cues your pushback provided.
“It’s because the explanation isn’t connected to anything that could be refuted.” This is one of the key understandings that comes from working with these systems. They are remarkably powerful but there’s no there there. Knowing this I’ve found enables more effective usage because, as the article is describing, you move from a mode of arguing with “a person” to shaping an output.
Do not argue with the LLM, for it is subtle and quick to anger, and finds you crunchy with ketchup.
These are, broadly, all context management issues - when you see it start to go off track, it's because it has too much, too little, or the wrong context, and you have to fix that, usually by resetting it and priming it correctly the next time. This is why it's advantageous not to "chat" with the robots - treat them as an english-to-code compiler, not a coworker.
Chat to produce a spec, save the spec, clear the context, feed only the spec in as context, if there are issues, adjust the spec, rinse and repeat. Steering the process mid-flight is a) not repeatable and b) exacerbates the issue with lots of back and forth and "you're absolutely correct" that dilutes the instructions you wanted to give.
This was my reading too. Interesting idea, but it took 10 pages of fluff to get to it and I didn't even believe the final idea when we got there. I started off reading the first part and thought he would get to the part where he realized he was managing context wrong. Never got there, instead he thought it was about the shape of the prompt.
The article looks like an AI generated novel to me. So I didn't bother reading it in detail. But I see telltale signs of long conversations leading to the agent cutting corners.
To the author (and those who write novel-like blogs): I suggest publishing the raw prompt you used to generate such slop instead. We'll have more respect for you if you respect the reader's time.
I got about halfway through this article until I started wondering why it was so long and going in loops. Then I ctrl+f'd.
` just `, (spaces on either side matter), 11 instances, most seem to be `isnt just`, `wasnt just`, `doesnt just` type pattern
`-`, an en dash instead of an emdash but 59 instances.
This article is either from a clanker and I am pissed off at wasting my time reading it, or from someone who writes like a clanker, and I am pissed off at wasting my time reading it.
> …
> When I write a prompt, the agent doesn’t just read the words. It reads the shape. A short casual question gets read as casual. A long precise document with numbered rules gets read as… not just the rules, but also as a signal. “The user felt the need to write this much.” “Why?” “What’s going on here?” “What do they really want?”
This is an interesting premise but based on the information supplied, I don’t think it’s the only conclusion. Yet the whole essay seems to assume it is true and then builds its arguments on top of it.
I’ve run into this dilemma before. It happens when there’s a TON of information in the context. LLMs start to lose their attention to all the details when there’s a lot of it (e.g. context rot[0]). LLMs also keep making the same mistakes once the information is in the prompt, regardless of attempts to convey it is undesired[1]
I think these issues are just as viable to explain what the author was facing. Unless this is happening with much less information
0. https://www.trychroma.com/research/context-rot
1. https://arxiv.org/html/2602.07338v1
If you ask a vague ignorant question, you get back authoritative summaries. If you make specific request, each statement is taken literally. The quality of the answer depends on the quality of the question.
And I'm not using "quality" to mean good/bad. I mean literally qualitative, not quantifiable. Tone. Affect. Personality. Whatever you call it. Your input tokens shape the pattern of the output tokens. It's a model of human language, is that really so surprising?
To me, after the novelty of seeing a computer program execute (more or less) what I ask in plain English wears off, what’s left is the chore of managing a bunch of annoying bots.
I don’t know yet if we’re more productive or not, if the resulting code is as good. But the craft in itself is completely different, much more akin to product managing, psychology, which I never enjoyed as much.
https://aphyr.com/posts/418-the-future-of-everything-is-lies...
In this case, there's no person to grow. It's an overly talkative calculator.
I never expected to see this number of engineers aspiring to emulate Dilbert's pointy haired boss.
Couldn’t you have a “communications” LLM massage your prompts to the “main” LLM so that it removes the queues that cause the main LLM to mistakenly infer your state of mind?
The fundamental idea is that "intelligence" really means trying to shorten the time to figure out something. So it's a tradeoff, not a quality. And AI agents are doing it.
Therefore, if that perspective is right, the issues that the OP describes are inherent to intelligent agents. They will try to find shortcuts, because that's what they do, it's what makes them intelligent in the first place.
People with ASD or ADHD or OCD, they are idiot-savants in the sense of that paper. They insist on search for solutions which are not easy to find, despite the common sense (aka intelligence) telling them otherwise.
It's a paradox that it is valuable to do this, but it is not smart. And it's probably why CEOs beat geniuses in the real world.
I'd also argue there's some training bias in the performance, it's not just smart shortcuts... Claude especially seems prone to getting into a 'wrap it up' mode even when the plan is only half way completed and starts deferring rather than completing tasks.
"Figure out" implies awareness and structured understanding. If we relax the definition too much, then puddles of water are intelligent and uncountable monkeys on typewriters are figuring out Shakespeare.
Paragraphs with just a single sentence.
I know it's associated with LLM writing. This article probably wasn't written by an LLM. But still. It has a kind of rhythm to it. Like poetry. But poetry designed to put me to sleep.
> If you try to refute it, you’ll just get another confabulation.
> Not because the model is lying to you on purpose, and not because it’s “resistant” or “defensive” in the way a human might be. It’s because the explanation isn’t connected to anything that could be refuted. There is no underlying mental state that generated “I sensed pressure.” There is a token stream that was produced under a reward function that prefers human-sounding, emotionally framed explanations. If you push back, the token stream that gets produced next will be another human-sounding, emotionally framed explanation, shaped by whatever cues your pushback provided.
“It’s because the explanation isn’t connected to anything that could be refuted.” This is one of the key understandings that comes from working with these systems. They are remarkably powerful but there’s no there there. Knowing this I’ve found enables more effective usage because, as the article is describing, you move from a mode of arguing with “a person” to shaping an output.
Do not argue with the LLM, for it is subtle and quick to anger, and finds you crunchy with ketchup.
These are, broadly, all context management issues - when you see it start to go off track, it's because it has too much, too little, or the wrong context, and you have to fix that, usually by resetting it and priming it correctly the next time. This is why it's advantageous not to "chat" with the robots - treat them as an english-to-code compiler, not a coworker.
Chat to produce a spec, save the spec, clear the context, feed only the spec in as context, if there are issues, adjust the spec, rinse and repeat. Steering the process mid-flight is a) not repeatable and b) exacerbates the issue with lots of back and forth and "you're absolutely correct" that dilutes the instructions you wanted to give.
It's just speedrunning context rot.
It’s an interesting thesis, it’s not well written or well told
I've experienced this my entire life and have all but given up trying to have actual conversations with people.
To the author (and those who write novel-like blogs): I suggest publishing the raw prompt you used to generate such slop instead. We'll have more respect for you if you respect the reader's time.
It's also kind-of their point that they find the information delivery more important than the prose; they're leaning into their situation :-D
` just `, (spaces on either side matter), 11 instances, most seem to be `isnt just`, `wasnt just`, `doesnt just` type pattern
`-`, an en dash instead of an emdash but 59 instances.
This article is either from a clanker and I am pissed off at wasting my time reading it, or from someone who writes like a clanker, and I am pissed off at wasting my time reading it.
> That’s confabulation. Not a metaphor. The same phenomenon.
> Published. Replicated. Not fringe.
> Not to validate it. Not to refute it. Not to engage with its content at all.
https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
Which is a shame coz the premise is interesting.