> One read-only command that inventories every AI agent, harness, MCP server, plugin, and AI extension on a machine
If this tool is returning even a single hit from this, you're probably using these agents wrong. You really want to run these in a way so they cannot touch your system drive/general filesystem that you use to do real work on. Even SOTA models at the end of their context limit behave REALLY illogical and does mistakes frequently. Don't run them straight on your machine unless you have backups and confirmed your backups work.
Aside from random errors made by the model, another reason to sandbox is that prompt injection could make your agent behave in an actively harmful manner.
This is the correct response, but it also feels as though ≈nobody is sandboxing their agents/harnesses in practice. Or at least, a good majority isn’t.
I think there is a cycle, like with most things of this nature.
You start really locked down, and you read and approve every request for access. You do this for a while, but never see a result you deny, so you stop reading as closely. You keep hitting that approve button. You start paying even less attention to it. You start feeling silly, like you are simply slowing the process down. You get frustrated, because you keep coming back to your session and realizing your agent has been stuck waiting for approval for a long time, and the task that would have been done by now hasn’t even started.
Now you are running even more simultaneous sessions, which means more and more of your agents are stuck waiting for your approval. You feel even sillier, because you are taking even less time now to review and approve requests, but your review step is causing more and more slowdowns because you have more sessions going so you take longer between approvals. You feel like you are spending most of your time cycling through sessions hitting approve. You still have not come across a request that was dangerous or would have caused an issue, so you feel more and more like you are wasting your time.
So you slowly start to give your agents more access with fewer review steps.
Maybe this results in a catastrophic failure at some point, or maybe it doesn’t.
AFAIK, I know plenty of frontend engineers that without blinking run "npm install" on random 3rd party projects they find on GitHub, and they do their banking and everything else on the same machine. But then again, I also know people (one person to be fair) who have unprotected sex with prostitutes, so maybe something makes me slightly biased here.
I mean yeah, there are a ton of people who are stupid enough to disregard all kind of safety precautions. That doesn't mean the safety precautions are invalid though.
I run it on a physically dedicated machine, it has a root there (through sudo nopassword) and I keep some of my stuff there too. I don't do it for security reasons, I just want it to run when my laptop is closed and I'm outside or what not.
Haven't seen any close calls so far. The thing just behaves. Nothing of the horror stories of eremerefing the whole home directory or a database. Am I just lucky?
I don't think you're just lucky, depends heavily on the model + reasoning effort. I've never had any of the GPT models do anything of the sorts when using the higher reasoning efforts, but sometimes when I play around with local models, even "big" ones (as big as you can fit with 96GB VRAM) sometimes forgets/misses to define $ID then do "rm -rf data/$ID" for example, deleting more than they intended.
>sometimes forgets/misses to define $ID then do "rm -rf data/$ID"
Right, that's kinda the same failure mode as delete from table something and hitting enter before you write the condition or writing the wrong one. If you reach the point where you opened the terminal to do it this way you already lost.
> If you reach the point where you opened the terminal to do it this way you already lost.
I'm not sure what this means, the model and agent harness is the ones "opening the terminal and running this" (via a exec_shell tool or whatever), they do mistakes like this sometimes. Sometimes the scope is bigger, sometimes less, but anything below SOTA + higher reasoning efforts seems to fall into these mistakes sometimes.
So do I, which is why I don't open the terminal to production database.
>anything below SOTA + higher reasoning efforts seems to fall into these mistakes sometimes.
We figured out how to deal with the human version of this mistake -- add a robust layer with build-in checks between the sloppy meatsack and production data and also make it annoying to access, so it does not become a habit and also requires high reasoning effort.
In my developer environment I can afford this mistake and can afford to delete everything I can touch. For some pieces the recovery will require more effort, but ultimately I can also have my laptop stolen on the way from the office.
On production however it will be a stack of forms to fill, a script with the dry-run and a four-eye rule to approve running it. We will also be able to recover production from the backup as we should. Or at least the company collectively believes so.
Seems like something that could also have enterprise applications for Shadow AI within an organization. Wouldn't be surprised if some of the observability/governance companies pick this up to use in their stack.
I need to wire a pixel-display to show emoji faces of the agents and how they roll their eyes and all of that. I have the display, I have the prompt to pick a mood from emotional vocabulary, a custom harness and all of that. I just can't figure out how to make the eyes move on a statically pre-rendered emoji.
> One read-only command that inventories every AI agent, harness, MCP server, plugin, and AI extension on a machine
If this tool is returning even a single hit from this, you're probably using these agents wrong. You really want to run these in a way so they cannot touch your system drive/general filesystem that you use to do real work on. Even SOTA models at the end of their context limit behave REALLY illogical and does mistakes frequently. Don't run them straight on your machine unless you have backups and confirmed your backups work.
Aside from random errors made by the model, another reason to sandbox is that prompt injection could make your agent behave in an actively harmful manner.
This is the correct response, but it also feels as though ≈nobody is sandboxing their agents/harnesses in practice. Or at least, a good majority isn’t.
I think there is a cycle, like with most things of this nature.
You start really locked down, and you read and approve every request for access. You do this for a while, but never see a result you deny, so you stop reading as closely. You keep hitting that approve button. You start paying even less attention to it. You start feeling silly, like you are simply slowing the process down. You get frustrated, because you keep coming back to your session and realizing your agent has been stuck waiting for approval for a long time, and the task that would have been done by now hasn’t even started.
Now you are running even more simultaneous sessions, which means more and more of your agents are stuck waiting for your approval. You feel even sillier, because you are taking even less time now to review and approve requests, but your review step is causing more and more slowdowns because you have more sessions going so you take longer between approvals. You feel like you are spending most of your time cycling through sessions hitting approve. You still have not come across a request that was dangerous or would have caused an issue, so you feel more and more like you are wasting your time.
So you slowly start to give your agents more access with fewer review steps.
Maybe this results in a catastrophic failure at some point, or maybe it doesn’t.
AFAIK, I know plenty of frontend engineers that without blinking run "npm install" on random 3rd party projects they find on GitHub, and they do their banking and everything else on the same machine. But then again, I also know people (one person to be fair) who have unprotected sex with prostitutes, so maybe something makes me slightly biased here.
I mean yeah, there are a ton of people who are stupid enough to disregard all kind of safety precautions. That doesn't mean the safety precautions are invalid though.
I run it on a physically dedicated machine, it has a root there (through sudo nopassword) and I keep some of my stuff there too. I don't do it for security reasons, I just want it to run when my laptop is closed and I'm outside or what not.
Haven't seen any close calls so far. The thing just behaves. Nothing of the horror stories of eremerefing the whole home directory or a database. Am I just lucky?
I don't think you're just lucky, depends heavily on the model + reasoning effort. I've never had any of the GPT models do anything of the sorts when using the higher reasoning efforts, but sometimes when I play around with local models, even "big" ones (as big as you can fit with 96GB VRAM) sometimes forgets/misses to define $ID then do "rm -rf data/$ID" for example, deleting more than they intended.
>sometimes forgets/misses to define $ID then do "rm -rf data/$ID"
Right, that's kinda the same failure mode as delete from table something and hitting enter before you write the condition or writing the wrong one. If you reach the point where you opened the terminal to do it this way you already lost.
> If you reach the point where you opened the terminal to do it this way you already lost.
I'm not sure what this means, the model and agent harness is the ones "opening the terminal and running this" (via a exec_shell tool or whatever), they do mistakes like this sometimes. Sometimes the scope is bigger, sometimes less, but anything below SOTA + higher reasoning efforts seems to fall into these mistakes sometimes.
>they do mistakes like this sometimes.
So do I, which is why I don't open the terminal to production database.
>anything below SOTA + higher reasoning efforts seems to fall into these mistakes sometimes.
We figured out how to deal with the human version of this mistake -- add a robust layer with build-in checks between the sloppy meatsack and production data and also make it annoying to access, so it does not become a habit and also requires high reasoning effort.
In my developer environment I can afford this mistake and can afford to delete everything I can touch. For some pieces the recovery will require more effort, but ultimately I can also have my laptop stolen on the way from the office.
On production however it will be a stack of forms to fill, a script with the dry-run and a four-eye rule to approve running it. We will also be able to recover production from the backup as we should. Or at least the company collectively believes so.
"Even SOTA models at the end of their context limit behave REALLY illogical "
The trick is not go to that limit, but stay under 50% or even better 25% of context length. But backups are a smart thing anyway.
Seems like something that could also have enterprise applications for Shadow AI within an organization. Wouldn't be surprised if some of the observability/governance companies pick this up to use in their stack.
Wes McKinney has a project for agent visibility: https://www.agentsview.io/
"Geiger"
Prepping for AI apocalypse I see
Haha The radiation was already there — I just built the counter. :)
I need to wire a pixel-display to show emoji faces of the agents and how they roll their eyes and all of that. I have the display, I have the prompt to pick a mood from emotional vocabulary, a custom harness and all of that. I just can't figure out how to make the eyes move on a statically pre-rendered emoji.
Nice!