rajvarkala

Not sure I follow. But, the first paragraph is interesting.

You are saying, employees stick around if they are given easy tickets, and companies care about passing along easy tickets so warm bodies do not churn.

That will be a big claim.

If the AI is never going to be manageable, never trustable, then the whole idea of agentic systems is dumb.

What is the point of an agent running and you don't trust it?

That would be equivalent to calling this whole AI wave useless. May be it is, maybe it is not.

Indeed. The whole AI game is predicated on the fact that they can deliver work equivalent to humans in some cases. If that is never going to be the case, then this whole agentic stuff goes belly-up.

The alternative scenario is they get better and do some work really well. That is an interesting territory to focus on.

How do you verify AWS charges? By inspecting logs? There goes the arbiter.

I get the binary part. The biggest difference is the subjective component of outcome? However, a tech provider - especially Agent provider - has to bring down the subjective to a quantitative metric when selling. If that cannot be done, I am not sure what we are going to be buying from Agent builders/providers?

:)

One reason slack exists is because of capacity and utilization, less slack -> higher wait times in peak times.

Is slack intended for Employee welfare? Come on, we are talking corporate here.

The support services are already regimented - L1, L2 etc. I am not a fan of AI either, but it may be a new reality.

We just give today's human performance metrics to AI agents.

AI agent developers internally have a metric they are targeting to improve. That itself violates goodhart law.

Interesting. Let's take the case of infra spend on AWS. Amazon says you invoked serverless calls 100k times and you are charged for it. How are you trusting them?

Understood. So, a better way is to keep him on a retainer? Or let Amazon or Cheaper store do a cost-plus model?

I think that is the core of the argument. It is the risk-sharing between buyer and seller. If sold on outcomes, seller carries all risk. If sold on work-put-in, buyer carries all risk.

Add to that, in some scenarios, outcomes themselves are fuzzy.

The price will be what you are willing to pay. No justification required, excepting for fairness (info asymmetry and what else?). It is written by me. Unfunded bootstrapped !!call it dire straits.

The agent builder loses contract .. Is this not force enough to make AI worthwhile?

:)

Of course, there is displacement. Jobs evolve.

Yes. Always exists. There neesd to be a secondary mechanism to verify .

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.

This is actually what I thought. Although, AI agent developers can capture 1:10 of value delivered - assuming AI agents deliver - but with competiton among Agent builders, the value capture will go down. That is one possibility

oversimplified surely, sweeping assumptions....

As much as I hate the assumptions, the worst case scenario is that AI is surely affecting some jobs.

I think what you described would be a good definition of outcome. But, Who bills customers that way if you think about software providers? The prevailing models are fixed fee , hourly fee or infra-spend fee.

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.

Sure, incentives can be gamed.

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.

The same applies to human agents as well. Humans are incentivised differently ? How?

The same oversight mechanism that applies to humans cannot correct the flaws of AI agents?

Hi alberth,

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

I often go back to customer support voice AI agent example. Let's say, The bot can resolve tickets successfully at a certain rate . This is capturable easily. Why is this difficult? What cases am I missing?

Understood.

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?

Some definitions of outcomes I believe are very easy to determine. Even if the outcomes are easy to determine, the outcomes I am certain are negotiated between the buyer and seller, just as a service contract.

There could be some workflows where outcomes may not be immediately delivered. Take for instance, a recruiting AI agent solving a successful hire. I would be very glad to understand those scenarios in the field.

Hey ,

As costs vary, I think it becomes even more important to track costs for each task.

Also, if costs also vary a lot without usefulness, it becomes difficult to pass these costs onto the customer.

Hence pricing needs to take care of volatility in cost as well. However, pricing is mostly dependent on the value AI Agents create. That I think, AI builders need to generate somehow, instead of just activity.

I am curious to know your opinions to the questions I am thinking about.

What do you think are the challenges with AI Agents pricing? How you are currently pricing your AI Agents work?

Would be great to know what HN community thinks.

It'd be amazing to receive feedback and thoughts.

Hi, Co-Founder of valmi.io here. Airbyte protocol is a spec derived from singer.io and it is a way to interact with and launch docker containers. Moreover, airbyte_cdk is under MIT license. Could you please elaborate on what kind of permission you are referring to?

Regards, Raj