Principles for Building One-Shot AI Agents
23 comments
·April 16, 2025sebastiennight
NoTeslaThrow
Surely the major issue is thinking you've converged when you haven't. If you're unsure if you've converged you can just bail after n iterations and say "failed to converge".
bhl
Just give your tool call loop to a stronger model to check if it’s a loop.
This is what I’ve done working with smaller model: if it fails validation once, I route it to a stronger model just for that tool call.
behnamoh
> if it fails validation once, I route it to a stronger model just for that tool call.
the problem the GP was referring to is that even the large model might fail to notice it's struggling to solve a task and keep trying more-or-less the same approaches until the loop is exhausted.
sebastiennight
Exactly. You'd still be in a non-deterministic loop, just a more expensive one.
randysalami
I think we need quantum systems to ever break out of that issue.
EDIT: not as to creating an agent that can do anything but creating an agent that more reliably represents and respects its reality, making it easier for us to reason and work with seriously.
sebastiennight
Could you share the logic behind that statement?
Because here I'm getting "YouTuber thumbnail vibes" at the idea of solving non-deterministic programming by selecting the one halting outcome out of a multiverse of possibilities
dullcrisp
ELI40 “YouTuber thumbnail vibes?”
randysalami
That would be some Dr. Strange stuff. I’m just saying a quantum AI agent would be more grounded when deciding when to stop based on the physical nature of their computation vs. engineering hacks we need for current classical systems that become inherently inaccurate representations of reality. I could be wrong.
devmor
I don’t believe quantum computers can solve the halting problem, so I don’t think that would actually help.
This issue will likely always require a monitor “outside” of the agent.
randysalami
I think you’re right that they can’t “solve” the halting problem but are more capable at dealing with it than classic ai agents and more physically grounded. Outside monitoring would be required but I’d imagine less so than classical systems and in physically different ways; and to be fair, humans require monitoring too if they should halt or not, haha.
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robszumski
Author of the post, love to see this here.
Curious what folks are seeing in terms of consistency of the agents they are building or working with – it's definitely challenging.
TZubiri
What is a “one-shot” AI Agent? A one-shot AI agent enables automated execution of a complex task without a human in the loop.
Not at all what one-shot means in the field. Zero-shot, one-shot and many-shot means how many examples at inference time are needed to perform a task
Zero shot: "convert these files from csv to json"
One shot: "convert from csv to json, like "id,name,age/n1,john,20" to {id:"1",name:"tom",age:"20"}
devmor
Given the misunderstandings and explanation of how they struggled with a long-solved ml problem, I believe this article was likely written by someone without much formal experience in AI.
This is probably a case where some educational training could have saved the engineer(s) involved a lot of frustration.
zavec
As a casual ML non-practicioner, what was the long-solved ML problem they ran up against?
robszumski
Fair criticism. I was going for the colloquial usage of "you get one shot" but yeah I did read that Google paper the other day referring to these as zero-shot.
lerp-io
u can’t one shot anything, you have to iterate many many times.
canadiantim
You one-shot it, then you iterate.
Sounds tautological but you want to get as far as possible with the one-shot before iterating, because one-shot is when the results have the most integrity
> A different type of hard failure is when we detect that we’ll never reach our overall goal. This requires a goal that can be programmatically verified outside of the LLM.
This is the largest issue : using LLMs as a black box means for most goals, we can't rely on them to always "converge to a solution" because they might get stuck in a loop trying to figure out if they're stuck in a loop.
So then we're back to writing in a hardcoded or deterministic cap on how many iterations counts as being "stuck". I'm curious how the authors solve this.