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As a general rule with LLMs, don't just tell it to do something if you actually need to make sure it gets done. Use a hook script to make it do that, or use the history that's already there (transcripts of all sessions are retained in ~/.claude, for example). There are innumerable scripts out there to parse these, or your agent will whip one up for you in 5 minutes.

“ I think this one is overly verbose and probably falls out of context windows pretty quickly”

It likely will. Half way through a session I routinely watch the agent append my rules to the top of its thinking only to do exactly what it said it wasn’t going to do after another minute of thinking.

It will then apologize profusely right before doing it again.

As others have said, use hooks.

I like to think of the initial system prompt as a fuse or bootloader. The user prompt and feedback from tool execution are where most of the alignment comes from in multi-turn use. You want to point the agent in the right direction and then get the hell out of its way.

If your agent isn't performing as expected but can otherwise see and describe the tools as you expect, your mental model of what the tools should be is probably wrong. Adjusting the system prompt can address this, but it quickly bloats and starts to turn into a game of whack-a-mole.

I've got an agent that talks to a very large data warehouse and the system prompt is somewhere around 100 tokens. Most of the important information lives in the user's request and in the environment.

I’ve done something similar for myself to learn Django. Claude Code has a built in Learning Mode, I extended that with a Coaching Mode. Where it is instructed on how to coach me, how to help stub out features, how to give feedback in code review, etc. With the main instruction being never to write code for me when in that mode. It can write basic logic examples/pseudo code and discuss different approaches to the problem. I have found it to be really effective, it is my go to for learning new things. I’m using it now to learn Elixir.

This is not long at all compared to what is actually being used in production by my agents and others like Claude Code...

I wish you good luck! And add that I'd be interested to hear how you get on. I intend to adopt a similar approach with my classes in September. The . history folder is a great idea.

How do you intend to assess your students?

Love it! I think the power of LLMs to acquire new skills and deepen the knowdledge is underestimated.

When used correctly, they offer a huge advantage over those who don't use them and think they understand but remain superficial. I encourage you to ask even the most obvious questions.

Learning mode has been a huge help for me, it quickly became my favorite way to learn. I ended up created a “Coaching Mode” output style that took some of the learning concepts like stubbing todos for the users and added other intructions that better fit how I learn

Same issue as with cliffnotes. Easy way out means the easy way will be taken. Unless, you actually design a decent assignment or exam. In person essays or exams, heavily weighted, you are simply screwed if you didn't study the old fashioned way. A couple of my more serious classes were like this: no homework, no projects, entire grade based on 3 exams. That put the fear of whatever diety you subscribe to into you like nothing else to study hard and not fall behind. One bad exam you can't really come back from. Better luck next year when you retake it. Or, you dig in like hell.

They're only cheating themselves in a world that increasingly cares about knowledge (market trend of seniors being preferable hires to fresh out of school juniors) and not the piece of paper that "proved" you had such knowledge.

Agreed. I don't know how they plan to enforce this but this is way better than some other articles that have come up indicating educational bans on AI use, in-person proctoring, verbal assessments, pen and paper exams etc. This is the first attempt at an approach I've seen that doesn't seek to isolate education from reality; students that are effective at integrating AI into their work and actually understand what they're doing are going to get jobs, which is ultimately the goal of school.

Yes absolutely! We linked your version inside the extended AI policy document, but forgot to add it to our website cs336.stanford.edu

Congrats. This seems like a great prompt to ensure a useful default experience. People should not confuse this with "anti cheating" and instead helping people learn how to learn.

Do you have further insights on AI and education since?

They reference the gist of 1cg in the honor code section of CS336.

https://cs336.stanford.edu/

To be fair, shipping these guidelines as AGENTS.md/CLAUDE.md in the repo that contains the assignments will make it so that agents will pick this up without needing students to opt in explicitly. Seems like a reasonable first step to me

We symlink AGENTS.md and CLAUDE.md to a single file in our repo

They won't, because forcing the file to be named after their product is an intentional marketing choice. Free advertising on every repo that has it.

> I really wish Anthropic would hurry up and teach Claude Code to check for that file too.

Surely such a trivial feature could be implemented in seconds using e.g. Claude? It's not about them not "hurrying up".

I wouldn't hold my breath.

I agree. Learning to code from scratch today would be difficult to say the least. The scar tissue accrued from debugging a compilable typo, a misplaced comma or parenthesis teaches something that's hard to recreate - but replacing it with durable learning that won't age-out is a definite win.

I think the answer to "how will AI agent guidelines be enforced" is that they won't be because they can't be, at least not directly.

This doesn't mean that this approach doesn't have value though. I think it very much does.

One way to indirectly enforce use of the AI agent guidelines is via an oral examination where the instructor and student look over their work together and talk about it. Students who have genuinely tried to learn and used AI as a learning tool via the agent guidelines should do a lot better in an oral exam than students who have used AI as a solution generator.

I adopted the oral exam (without agent guidelines) for a course i teach in the academic year just gone, it worked pretty well. Next term I intend to include the agent guidelines to give them clearer guardrails. Still ultimately optional, but if students choose to ignore them it's gonna be pretty obvious during our conversation.

Well, no amount of instructions would work if the student has no intention to learn anything.

Stanford has an honour code. Meant no oversight even during exams. Worked surprisingly well when I was there. The flipside is, if you’re ever caught cheating, there are no second chances.

I imagine this applies here, too, if they want to enforce it strictly.

In an ideal world guidelines should be suggestions for those willing to make the best of the course and improve as a person and professional. However a degree has real world value and repercussions, so enabling someone incompetent to do a dangerous job can put innocent lives in jeopardy. It's tough, but I hope in time we learn how to live with this new tech.

I know who Petzold is but I can't remember if I read this at the time. I do love it though. I was thinking a lot about generated code in Visual Studio a couple years after this article.

It's kinda funny to think about various forms of code generation. From compilers to IDEs to parser generators to, now, LLMs. Even several higher level languages that compile to lower level languages are generative, essentially.

Still not a fan of LLMs, but it's always a good to remember that the concept isn't entirely new or unique.

Assignments the agent is bad at seems like a losing battle. Just need to base the mark off the in person test, maybe keep 20-30% to encourage people to still do the assignments. Some will cheat but it will just be hurting them for the test.

Laptop without internet access, sure. Pencil and paper? that is brutal :)

I agree it's not a complete solution. But as those don't exist as a society we are looking for a step function in the right direction. and IMO this is one such step. You may disagree that it's not a very large step, but I would argue it's still in the right direction therefore it is neccesary, especially in education space, and I'm happy to see someone publishing at attempt.

Jailbreaking isn’t even needed - you can just modify the file

in the context of learning I think it's good to execute yourself then see what happens immediately, once you allow the agent to execute things, it tends to run several follow up commands when needed, an example from top of my head would be running a server that fails because the port is already in use, the agent will easily find the port and decide to kill it or not and then re-run the command, but if you run the command and see the error, then you get the chance to learn what's going on and how to fix it. You can still use the LLM to read the error and explain you why this happens, according to this guidelines.

Why wouldn't it be a part of the education?

zsh is fine, but I prefer fish. It has a funnier name!

> you can actually learn a LOT by being given the answer, if you actually care to learn.

Even if you "actually care to learn", this is a huge mental shortcut and you're deceiving yourself if you think deep learning is happening from looking at the answer.

On top of that, the pressures to just finish the coursework and move on to your other homework due tomorrow seems pretty high. Your suggestion means we're no longer coddling/shielding students, but we also aren't actively helping them, are we?

Stanford is a research university. The student should have full responsibility for learning outcomes. The university will provide support and opportunities to the extent its resources allow, but it's up to the student to choose if they want to take advantage of that. Those who need a more guided approach to learning can always go to teaching-oriented universities or find a personal tutor.

That's a major reason why employers have traditionally valued degrees from research universities, even if they are not particularly highly ranked. Being able to thrive in an environment like that shows a degree of independence and initiative.

My understanding is that research shows more learning happens when the student has to struggle with the material to solve problems and answer questions.

As an educator you have to trust that most students are there to learn, not to cheat. Some will cheat, of course, and you can try to stop it or detect it but it devolves into whack-a-mole and the cheater is probably more motivated than the instructor.

Once they have graduated they will be on the job using LLMs and agents all day long, and their employers not only won't care they will be encouraging or requiring it.

When calorie dense food and gas powered vehicles came on the scene, humans (generally) got fat and out of shape. "Why eat that salad and go for a run?" one might say, "This cheesecake tastes much better and I can just drive wherever I want to go."

Getting fat is one thing, but getting stupid is another, and I really fear for the future of humanity when it becomes so easy to sidestep the processes that let us actually learn and grow because stuff like "using agent ai coding is trivial".

using a coding tool is trivial, correct. so is using a microwave oven or its larger counter-parts. you need a certain level of person to know if what came out of it was Michelin-star or not and I do not think Stanford is going for Hot Pockets here.

I don’t really know why this is getting downvoted. It’s clear that higher education is degrading because of easy to reach AI solutions that have no type of penalties for use.

During my undergrad it was normal to see people refer to Chegg solutions to get their answers, or as a friend for theirs.

Maybe there’s a reason my first CS professor wrote out Java code with pencil and paper I guess.

Students are struggling to get work after graduating because they're dropped into a competitive environment. Ideals aren't enough to get jobs in the current environment.

Universities should be places which are at the bleeding edge of development and providing society with the best new ideas/tech, etc has to offer. Junior workers should be hotbeds of exciting talent which have the ability to revolutionise industries.

By creating such milquetoast environments to study in, which are seemingly scared or unable to prepare people for the future, students are being done a disservice.

Far too many people are far too comfortable with their cushty positions, and it's not doing the youth any favours.

Funny you should say that. This is about Stanford:

>In our tech-enabled, newly A.I.-powered world, students were increasingly fudging just about everything. They would embezzle dorm funds to spend on their friends and lie about having Covid to get the UberEats credits that the school offered to those in quarantine. Some kids I knew published a paper that claimed a groundbreaking new A.I. advancement. Online sleuths quickly pointed out that it appeared to be just a stolen Chinese model, to which the two Stanford co-authors responded by blaming the plagiarism on the third author.

>In junior year, 49 percent of the 849 computer science majors who responded to an annual campus survey said they would rather cheat on an exam than fail. A friend of mine captured the school’s ethos while we were discussing the tech hardware and other items our student club neglected to return to corporate sponsors. It was all, I recall her saying, “just a little bit of fraud.”

https://www.nytimes.com/2026/05/17/opinion/chatgpt-ai-colleg...

I mean, some would say that's how this whole thing got started.

> The solution is to scale the difficulty of the objective measures. Expect far more from students.

So now students are _required_ to use agents? That's a bit crazy

Would universities be paying the token cost? Is that you Dario?

Seeing my own kids (teens) go through some of this, I'm becoming slightly less pessimistic as it all shakes out. Among their peer groups there does seem to be an opinion forming that sure, anyone can just ask ChatGPT for quick answers on assignments, but actually knowing stuff is a bit of a "flex" that's respected.

I don't think you will achieve much that way. Suppose you pair me with a plumber, an area I know almost nothing about. The plumber is not able to finish anything 'trickier' because they have an ignoramus looking over their shoulder. Maybe I can learn some things by watching, having things explained to me as they work. On the other hand if I just walk away there's no way to tell from the final product. You gotta learn the fundamentals to at least a comparable level to be able to contribute. Same reason you couldn't just google everything on your phone in calculus.

You have to balance that with teaching the skills needed to understand the domain sufficiently to take over when the model gets things wrong.

What a ridiculous take. Just because solutions exist in the llm training data, doesn't make these problems 'toy' or 'easy'. The human 'engineering hardness' scale doesn't align with what an llm can and can't do.

As an employer, I want education to be robust from the ground up, not turn uni into an attempt to bootcamp whatever is hot today.

I don't think a 4 year postsecondary education is enough time to make a developer that can hit the ground running. Not if it's 100% of class time on CS theory. Nor if it were 4 years of vocational training and labwork that leaned heavy into AI. Nor some mix. We train on the job heavily, it's just not possible to fit everything into the sausage grinder.

So why not throw in some mandatory non-major electives? Take the time to do stuff that frustrates people who want uni to be a certificate mill. I don't care if green employees are experts at the exact narrow set of tools I use. I want them to be good at learning, and to have gotten most of the standard CS topics out of the way.