[AgentMatrixAI]

I'm not so optimistic as someone that works on agents for businesses and creating tools for it. The leap from low 90s to 99% is classic last mile problem for LLM agents. The more generic and spread an agent is (can-do-it-all) the more likely it will fail and disappoint.

Can't help but feel many are optimizing happy paths in their demos and hiding the true reality. Doesn't mean there isn't a place for agents but rather how we view them and their potential impact needs to be separated from those that benefit from hype.

just my two cents

[lairv]

In general most of the previous AI "breakthrough" in the last decade were backed by proper scientific research and ideas:

- AlphaGo/AlphaZero (MCTS)

- OpenAI Five (PPO)

- GPT 1/2/3 (Transformers)

- Dall-e 1/2, Stable Diffusion (CLIP, Diffusion)

- ChatGPT (RLHF)

- SORA (Diffusion Transformers)

"Agents" is a marketing term and isn't backed by anything. There is little data available, so it's hard to have generally capable agents in the sense that LLMs are generally capable

[chaos_emergent]

I disagree that there isn't an innovation.

The technology for reasoning models is the ability to do RL on verifiable tasks, with the some (as-of-yet unpublished, but well-known) search over reasoning chains, with a (presumably neural) reasoning fragment proposal machine, and a (presumably neural) scoring machine for those reasoning fragments.

The technology for agents is effectively the same, with some currently-in-R&D way to scale the training architecture for longer-horizon tasks. ChatGPT agent or o3/o4-mini are likely the first published models that take advantage of this research.

It's fairly obvious that this is the direction that all the AI labs are going if you go to SF house parties or listen to AI insiders like Dwarkesh Patel.

[ashwindharne]

'Agents' are just a design pattern for applications that leverage recent proper scientific breakthroughs. We now have models that are increasingly capable of reading arbitrary text and outputting valid json/xml. It seems like if we're careful about what text we feed them and what json/xml we ask for, we can get them to string together meaningful workflows and operations.

Obviously, this is working better in some problem spaces than others; seems to mainly depend on how in-distribution the data domain is to the LLM's training set. Choices about context selection and the API surface exposed in function calls seem to have a large effect on how well these models can do useful work as well.

[mumbisChungo]

My personal framing of "Agents" is that they're more like software robots than they are an atomic unit of technology. Composed of many individual breakthroughs, but ultimately a feat of design and engineering to make them useful for a particular task.

[paradite]

Agents have been a field in AI long since 1990s.

MDP, Q learning, TD, RL, PPO are basically all about agent.

What we have today is still very much the same field as it was.

[lossolo]

Yep. Agents are only powered by clever use of training data, nothing more. There hasn't been a real breakthrough in a long time.

[posix86]

But that's how progress works! To me it makes sense that llms first manage to do 80% of the task, then 90, then 95, then 98, then 99, then 99.5, and so on. The last part IS the hardest, and each iteration of LLMs will get a bit further.

Just because it didn't reach 100% just yet doesn't mean that LLMs as a whole are doomed. In fact, the fact that they are slowly approaching 100% shows promise that there IS a future for LLMs, and that they still have the potential to change things fundamentally, more so than they did already.

[camdenreslink]

But they don’t do 80% of the task. They do 100% of the task, but 20% is wrong (and you don’t know which 20% without manually verifying all of it).

So it is really great for tasks where do the work is a lot harder than verifying it, and mostly useless for tasks where doing the work and verifying it are similarly difficult.

[posix86]

I would go so far as to say that the reason people feel LLMs have stagnated is precisely because they feel like they're only progressing a few percentage points between iteration - despite the fact that these points are the hardest.

[wslh]

> Can't help but feel many are optimizing happy paths in their demos and hiding the true reality.

Even with the best intentions, this feels similar to when a developer hands off code directly to the customer without any review, or QA, etc. We all know that what a developer considers "done" often differs significantly from what the customer expects.

[risyachka]

>> many are optimizing happy paths in their demos and hiding the true reality

Yep. This is literally what every AI company does nowadays.

[Forgeties79]

>The more generic and spread an agent is (can-do-it-all) the more likely it will fail and disappoint.

To your point - the most impressive AI tool (not an LLM but bear with me) I have used to date, and I loathe giving Adobe any credit, is Adobe's Audio Enhance tool. It has brought back audio that prior to it I would throw out or, if the client was lucky, would charge thousands of dollars and spend weeks working on to repair to get it half as good as that thing spits out in minutes. Not only is it good at salvaging terrible audio, it can make mediocre zoom audio sound almost like it was recorded in a proper studio. It is truly magic to me.

Warning: don't feed it music lol it tries to make the sounds into words. That being said, you can get some wild effects when you do it!

[skywhopper]

Not even well-optimized. The demos in the related sit-down chat livestream video showed an every-baseball-park-trip planner report that drew a map with seemingly random lines that missed the east coast entirely, leapt into the Gulf of Mexico, and was generally complete nonsense. This was a pre-recorded demo being live-streamed with Sam Altman in the room, and that’s what they chose to show.

[j_timberlake]

I mostly agree with this. The goal with AI companies is not to reach 99% or 100% human-level, it's >100% (do tasks better than an average human could, or eventually an expert).

But since you can't really do that with wedding planning or whatnot, the 100% ceiling means the AI can only compete on speed and cost. And the cost will be... whatever Nvidia feels like charging per chip.

[guluarte]

yep, the same problem with outsourcing, getting the 90% "done" is easy, the 10% is hard and completely depends on how the "90%" was archived

[ankit219]

Seen this happen many times with current agent implementations. With RL (and provided you have enough use case data) you can get to a high accuracy on many of these shortcomings. Most problems arise from the fact that prompting is not the most reliable mechanism and is brittle. Teaching a model on specific tasks help negate those issues, and overall results in a better automation outcome without devs having to make so much effort to go from 90% to 99%. Another way to do it is parallel generation and then identifying at runtime which one seems most correct (majority voting or llm as a judge).

I agree with you on the hype part. Unfortunately, that is the reality of current silicon valley. Hype gets you noticed, and gets you users. Hype propels companies forward, so that is about to stay.