Sounds great in theory, until you realize everyone has a different definition of outcome.
Take for instance, customer support Agent , that is supposed to resolve tickets. Assuming it resolves around 30% tickets by an objective measure. Do you think that cannot be captured and agreed upon by both sides?
The same oversight mechanism that applies to humans cannot correct the flaws of AI agents?
In many cases though, you don’t know whether the outcome is correct or not but we just have evals for that.
Our product is a SOTA recall-first web search for complex queries. For example, let’s say your agent needs to find all instances of product launches in the past week.
“Classic” web search would return top results while ours return a full dataset where each row is a unique product (with citations to web pages)
We charge a flat fee per record. So, if we found 100 records, you pay us for 100. Of its 0 then it’s free.
Or just my writing style?
Edit:
Well, actually - this kind of writing style does feel quite AI-ish:
> It really makes sense, and the best part — customers love it
I believe it was under British rule, they offered a reward for people bringing in dead cobras as proof of culling. Which worked until people started breeding them just to get the reward. Humans gamed the system and it made the problem worse.
The same oversight mechanism that applies to humans cannot correct the flaws of AI agents? What do you think is the catch?
I am not saying things are clearly defined in most settings. But my accounting agent ( real person) gets paid only when he files my tax returns.
If you finetune a model and it starts misbehaving, what are you going to do to it exactly? PIP it? Fire it? Of course not. AIs cannot be managed the same ways as humans (and I would argue that's for the best). Best you can do is try using a different model, but you have no guarantee that whatever issue your model has is actually solved in the new one.
There's no LLM equivalent.
Does “outcome billing” amount to anything different?
There is an argument to be made that SaaS tools tap the tool budget whereas AI agents can tap the worker budget of companies.
I am looking to understand more nuances here.
> If AI agents help each support employee handle 30% more tickets, that's like adding 30 new hires to a 100-person team, without the cost.
I think this is an oversimplification designed to make LLMs seem more profitable than they actually are.
As much as I hate the assumptions, the worst case scenario is that AI is surely affecting some jobs.
Oh! Yknow that thing we were charging you $200 a month for now? We're going to start charging you for the value we provide, and it will now be $5,000 a month.
Meanwhile, the metrics for "value" are completely gamed.
Well, of course. One of the huge advantages of agents is that they will actually help you to almost any extent game metrics.
Unlike people, who have ...
This is risking the end customer experience for your Agent buyer, which might not be worth the risk to a company that wants to keep customers very happy.
But, again, such systems already exist. The folk theorem guarantees this. In a repeated game, people crave reputation.
For instance, seller over-resolving will suffer in the long run, I guess.
Maybe the pricing model makes sense in the beginning.
Until people will realize the big secret - AI is still just software.
A new category of software.
The price of software generally only goes in one direction, and that’s a race to the bottom.
alberth•1h ago
And how do you programmatically measure it?
nerdjon•1h ago
\s (mostly because you know this will be the "Solution" that many will just run with despite the very real issue of how "persuadable" these systems are)...
The real answer is that even that will fail and there will have to be a feedback loop with a human that will likely in many cases lead to more churn trying to fix the work the AI did vs if the human just did it in the first place.
Instead of focusing on the places that using an AI tool can truly cut down on time spent like searching for something (which can still fail but at least the risk when a failure is far lower vs producing output).
malux85•1h ago
But for anything that’s not this trivial example, the person who knows the value most accurately is … the customer! Who is also the person who is paying the bill, so there’s strong financial incentive for them not to reveal this info to you.
I don’t think this will work …
rajvarkala•1h ago
rajvarkala•1h ago
I'd assume an outcome is a negotiated agreement between buyer and Agent provider.
Think of all the n8n workflows. If we take a simple example of Expense receipt processing workflows, or a lead sourcing workflow, I'd think the outcomes can be counted pretty well. In these cases, successfully entered receipts into ERP or number of Entries captured in salesforce.
I am sure there are cases where outcomes are fuzzy, for instances employer-employee agreement.
But in some cases, for instance, my accounting agent would only get paid if he successfully uploads my tax returns.
Surely not applicable in all cases. But, in cases Where a human is measured on outcomes, the same should be applicable for agents too, I guess
higginsniggins•1h ago