I'm betting against AI agents, despite building them

Agents have captivated the minds of groups of people in each large engineering org. I have no idea what their goal is other then they work on “GenAI”. For over a year now they have been working on agents with the promise that the next framework that MSFT or Alphabet publishes will solve their woes. They don’t actually know what they are solving for except everything involves agents.

I have yet to see agents solve anything but for some reason this idea that having an agent that you can send anything and everything will solve all problems for the company. LLMs have a ton of interesting applications but agents have yet to grasp me as interesting, I also don’t understand why so many large companies have focused time around it. They are not going to be cracking the code ahead of a commercial tool or open source project. In the time spent toying around with agents there are a lot of interesting applications that could have built, some of which may be technically an agent but without so much focus and effort on trying to solve for all use cases.

Edit: after rereading my post wanted to clarify that I do think there is a place for tool call chains and the like but so many folks I have talked to first hand are trying to create something that works for everything and anything.

Just because we'd love to have fully intelligent, automatic agents, doesn't mean the tech is here. I don't work on anything that generates content (text, images, code). It's just slob and will bite you in the ass in the long run anyhow.

I've gone from skeptical to willing to humor to "yeah this is probably right" in about 5 months, basically I believe: if you scope the subject matter very very well, and then focus on the tooling that the model will require to do it's task, you get a high completion rate. There is a reluctance to lean into the non deterministic nature of the models, but actually if you provide really excellent tooling and scope super narrowly, it's generally acceptably good.

This blog post really makes the tooling part seem hard, and, well... it is, but not that hard - we'll see where this all goes, but I remain optimistic.

But still not once have I seen an actual agent in the wild doing concrete work.

A “No True Agent” problem if you will.

I'd imagine future agents will include training to design these checks into any output, validating against the checks before proceeding further. They may even include some minor risk assessment beforehand, such as "this aspect is crucial and needs to be 99% correct before proceeding further".

The fundamental difference is we need HITL to reduce errors instead of HOTL which leads to the errors you mentioned

And I have an AI workflow that generates much better posts than this.

It's hard to make *one* good product (see startup failure rates). You couldn't make 12 (as seemingly a solo dev?) and you're surprised?

we've been working on Definite[0] for 2 years with a small team and it only started getting really good in the past 6 months.

0 - data stack + AI agent: https://www.definite.app/

I was under the impression that some kind of caching mechanism existed to mitigate this

If 50% of training data is not factually accurate, this needs to be weeded out.

Some industries require a first principles approach, and there are optimal process flows that lead to accurate and predictable results. These need research and implementation by man and machine.

It seems the author never used prompt/workflow optimization techniques.

LLM-AutoDiff: Auto-Differentiate Any LLM Workflow https://arxiv.org/pdf/2501.16673

> AI tools aren't perfect yet. They sometimes make mistakes, and they can't always understand what you are trying to do. But they're getting better all the time, In the future, they will be more powerful and helpful. They'll be able to understand your code even better, and they'll be able to generate even more creative ideas.

From another post on the same site. [0]

Yup, slop.

[0]: https://utkarshkanwat.com/writing/review-of-coding-tools/

This phrase is usually followed by some, you know...Math?

Also, if you look at any human process you will realize that none of them have a 100% reliability rate. Yet, even without that we can manufacture e.g. a plane, something which takes millions of steps, each without a 100% success rate.

I actually think the article makes some good points, but especially when you are making good points it is unnecessary to stretch credibility with exaggerating your arguments.

This is not remotely true. Think of any business process around your company. 99.9% availability would mean only 1min26 per day allowed for instability/errors/downtime. Surely your human collaborators aren't hitting this SLA. A single coffee break immediately breaks this (per collaborator!).

Business Process Automation via AI doesn't need to be perfect. It simply needs to be sufficiently better than the status quo to pay for itself.

"We can't allow this post to create FUD about the current hype on AI agents and we need the scam to continue as long as possible".