The AI agent economy: who gets paid, who buys and who decides

The AI agent economy is the system in which software agents do work, buy, sell and pay on behalf of people and companies. Work gets measured by tasks completed, where it used to be measured in hours. Companies that already run on agents, with clear rules and a person approving anything that moves money, start ahead.

I'm writing this from inside Daleki Global, the holding company where I build my businesses. Today agents handle most of the daily work and I govern from the board. In another post I explained why I believe agents will be the operating team and people will be the board. This time I want to look at another layer: the money, the pricing and the transactions that appear once agents are doing the work.

What is the AI agent economy?

It's the set of exchanges that happen when an AI agent works, buys or negotiates on someone's behalf. An agent gets a goal, uses tools and data, and delivers a result. If that result has value, someone pays for it. If the agent needs something to finish the job, it buys it.

For years, business software was a tool a person used. You paid for a license per seat, and the value depended on the hours your team put into it. Agents change the unit. What gets measured now is the finished task: a quote sent, a report reconciled, a supplier vetted.

That forces you to rethink how you charge and how you hire. I see it every day, because I built the Daleki Global system myself and agents take the repetitive work inside it. These days I rarely ask how many people a task needs. I ask what it costs to get it done right, and who reviews it.

How does running a business on agents change your costs?

Costs follow usage now, where they used to follow payroll. A traditional company hires when sales go up. A company that runs on agents adds capacity by paying for the work that actually gets executed, so it can grow while its structure stays lean.

I work with specialized vendors and avoid building large traditional teams, and I run each company as its provider. That makes me look at every expense with one question: what result did this give me? Daleki Lab projects follow the same rule. We compare cost against results, keep what works and shut off what doesn't.

A mistake I see often is assuming an agent is cheap by definition. A poorly designed agent burns resources, repeats mistakes and creates review work nobody budgeted for. The savings come when the process is well understood before you automate it, which is why my projects start with a diagnosis of the real operation.

There's a human side to this. As the cost of executing operational tasks drops, roles that exist only to execute those tasks will have to evolve. I believe leaders should back those people while they learn to direct agents, and not replace them without warning.

What happens when your customer sends an agent to buy?

A machine will read your offer before a person does. If an agent compares vendors for a purchasing manager, it will favor the one with clear pricing, written terms and predictable response times. Vague information makes it hesitate, and a hesitant agent moves on to the next vendor.

I already think this way in my companies. Daleki Trade promises to put the total cost in writing before you pay. That clarity helps a business owner importing goods today, and tomorrow it will help an agent comparing options. At Daleki Merch, customers get a quote over WhatsApp in under one business hour; when the one asking is an agent, that speed will count for even more.

For any company, the practical exercise is checking whether your catalog, pricing and terms make sense without a phone call. A lot of B2B commerce in Latin America still runs on conversation and relationships, and it will keep doing so because trust is human. The first comparison, though, will be done by an agent more and more often.

Marketing moves too. With Wingman we've spent more than a decade building communities for brands, and I think community gains value here. An agent can build the shortlist, but the final call still belongs to a person who already trusts a brand.

What money will agents use to pay each other?

Nobody has settled this yet. My conviction is that Bitcoin and other digital assets will become the rails for transactions between agents. An agent can't open a bank account or sign at a counter. It can operate with programmable money that moves over the internet, at any hour and across borders.

When Satoshi Nakamoto published the Bitcoin paper in 2008, it described a peer-to-peer electronic cash system that doesn't go through a financial institution. It was designed for people. The same logic fits software that needs to pay other software small amounts many times a day.

I got into crypto in 2020, and today my work in digital assets is Daleki Capital, which manages private funds with written rules and every risk disclosed. I'll say it as plainly here as I do there: digital assets are volatile and can lose much of their value. In 2022 my portfolio dropped more than 60%. What I'm sharing is a view on infrastructure and is not financial or investment advice.

My suggestion to any company is to study the subject before the need arrives. Learning what a wallet is, how a smart contract works and which controls an automated payment needs takes time. Better to learn it calmly than under pressure.

Which decisions should an AI agent never make?

An agent shouldn't decide on its own what a brand publishes, what gets claimed about results, prices and terms with customers, anything involving someone else's money, or hiring and letting people go. In my companies a person reviews all of that before it leaves the building.

Much of the AI agent economy will come down to designing those limits well. An agent with payment access needs a spending cap, a log of every transaction and a step where someone approves. If it talks to customers, it has to know what it can promise and what it can't. Write these rules before you turn the system on, not after the first incident.

Managing capital taught me that written rules protect you from impulsive decisions, and agents are no different. Autonomy grows as the agent proves it delivers, and it gets pulled back when it fails. I call this way of operating AI-native: AI in the design from day one and human judgment on what matters.

How can a company in Latin America prepare for the agent economy?

By starting with its own operation. Before thinking about agents that buy or sell outside, a company needs agents working inside, with organized data and clear rules. Everything external gets built on that foundation.

This is the order I follow, in my own companies and with the people I work with:

• Map the processes that eat the most time and figure out where your data lives.
• Write down what each agent does, what it can't do and who approves.
• Build a small pilot with real data and measure cost against results.
• Check whether your offer makes sense without a call: pricing, terms and timelines.
• Study digital payments calmly, with a clear view of the risks.

In Ecuador and across Latin America I see an advantage that often goes unnoticed. Many mid-sized companies don't carry heavy legacy systems they'd need to migrate, so they can design a lean operation from the start. One person with vision and a team of agents can compete with much larger structures.

If you want Daleki Lab to build that system for your company, the path is an AI project that starts with a diagnosis of your operation. If you'd rather learn to build it yourself with my guidance, there's one-on-one advisory. Either way, tell me where your company stands today and we'll figure out where to start.