What it means for you: a commentary on Sequoia Capital's essays about AI that sells work (2024 and 2026).
You can buy an AI agent for your company in two ways. Either you pay for a tool your person operates, or for finished work the agent hands over. For a company running on Excel and a full inbox, the second usually makes more sense, but only for work that can be checked.
The venture firm Sequoia Capital is pushing this idea today. Below is what it actually says, where we agree, where we do not and what it looks like in our work. If you are not sure what an agent is yet, start with What is an AI agent?
What does Sequoia say about AI agents?
In March 2026 Julien Bek of Sequoia wrote the essay Services: The New Software. His main argument: companies spend about six times more on services than on software. So the biggest AI companies will not sell tools, they will sell finished work.
Bek sums it up in one line:
A copilot sells the tool. An autopilot sells the work.
A copilot is a tool in the hands of a professional who is responsible for the result. An autopilot delivers the result straight to the company that needs it. He advises starting with work companies already pay outsiders for.
The older piece Generative AI's Act o1 by Sonya Huang and Pat Grady (October 2024) points the same way. In it, an agent is paid per resolved case, not per user. The authors also describe the path: first deploy AI with a person who checks it, and only with practice let it work alone.
Our view
We agree with the core. An owner of a Slovak or Czech company does not want another tool people have to learn. He wants the invoices posted and the emails answered.
Do not sell the app, sell the outcome. The software is just the tool. For the team, the outcome should be an efficiency gain.
Filip Papranec
founder of Digitaliko
The examples do not fit. Sequoia writes about US lawyers, insurance brokers and auditors. A company with 20 people in the office has no contracts department. Its work is smaller and scattered: emails, orders, invoices, retyping between systems.
"Finished work" is not a switch. We have not seen an agent that works alone from day one actually work. The Huang and Grady path, first with a person and then alone, is exactly what we do.
A tool or finished work: what is the difference for your company?
The difference is not the technology. It is who does the work and who is responsible for the result.
You pay for a tool
You pay for finished work
What you buy
A licence for every user
Handled emails, posted invoices
Who does the work
Your person, helped by AI
The agent, a person only on exceptions
Who checks the result
The same person at every step
Rules, plus a person on unsure cases
What you need
People's time to learn it
A written process, examples, a person for exceptions
When it pays off
The work is different every time
The work repeats daily and can be checked
Most common risk
Nobody uses the tool after a month
The agent answers wrongly in your name
Most companies buy the left column today, because they know it from SaaS. The right column needs more preparation, but saves more time. How an agent differs from plain automation is covered in AI agent or automation: what do you need?
Filip decides this for himself in a simple way:
When I understand something, I use AI and check the result myself. When I don't, I pay someone who does. They may well use AI too.
The client did not buy a tool they had to learn. They got answered emails. In the first weeks, though, the agent only drafted, and we sent the replies by hand.
The person did not leave the process. Customer support handles the complex cases and the ones the agent is not sure about. It is similar with invoices in our own accounting: 70 % go through without a person, a person checks the rest.
Our view
Here we part ways with Sequoia on emphasis. For an investor, the interesting part is that the person leaves the process. For an owner, what matters more is who catches the remaining cases. Finished work without a person on the exceptions is just a faster way to make a mistake in your name.
One more thing. Sequoia describes paying per resolved case. We do not bill that way today. We work on a monthly subscription and the solution is yours from day one.
What to do this month?
01
Pick one job
One that a single person does all day today, or that you pay someone outside for. For example customer emails, retyping invoices or orders from email.
02
Measure it
How many cases a week, how many minutes each, how many of them are exceptions. Without these numbers you cannot judge any offer.
03
Ask about the result, not the features
What happens to an unsure case? Who gets it? How will you measure that the agent works well?
Not sure whether a tool or finished work pays off for you?
Tell us which work you want gone. We get back to you within two business days, and together we pick what to fix first. We will also tell you if a tool is enough.
Is an AI tool better, or an agent that does the work?
It depends on the work. If it is different every time and judgement decides, buy a tool for a person. If it repeats every day and the result can be checked, finished work with a person on the exceptions pays off.
Will an AI agent replace an employee?
Usually not a whole one. It takes the repeated part of the work and leaves the exceptions to a person. For a client with an online shop, the agent answers customers within 5 minutes and customer support now handles only the complex cases.
Do you charge per handled case?
No. We work on a monthly subscription you can cancel any month. The solution is yours from day one, so the agent keeps running even if we stop working together.
Where should a company start with an AI agent?
With work one person does all day today, or that you pay someone outside to do. Count the cases per week and the exceptions. The agent first only drafts and a person approves; only then does it send on its own.
Sources: Julien Bek, "Services: The New Software", Sequoia Capital, 5 March 2026, sequoiacap.com. Sonya Huang and Pat Grady, "Generative AI's Act o1", Sequoia Capital, 9 October 2024, sequoiacap.com. The retelling is ours; Sequoia has nothing to do with this article.
Ask AI about this article
The AI also draws on our other articles and experience from real projects.
If the AI bubble bursts, Sequoia's David Cahn expects investors to take most of the loss. For a company the risk sits elsewhere: the AI tool vendor. What protects you, a checklist and where it does not apply.
AI now writes code from a plain sentence and the price of AI falls fast every year. But only the code got cheap. What Karpathy and Altman say, what got cheaper, what did not, and what to do before going live.
When an AI agent does the work, per-seat SaaS pricing stops fitting. What a16z and Bessemer write about paying for outcomes, where we disagree and what to ask your vendor.
LinkedIn
Real experiences, no hype
Filip Papranec, founder of Digitaliko. On LinkedIn we tackle technical challenges, share development know-how and show how AI actually changes business. We don't do AI hype: we're realists, not dream sellers.