Imagine asking an AI: “Help me choose an EV for my family with a budget of no more than THB 1.5 million. Compare the best options and take care of the booking.”
Today, AI can already research models, compare prices, evaluate driving range, find promotions, and locate dealers. But the moment we say “take care of the booking,” a new question emerges.
Would we allow AI to pay a THB 5,000 deposit on our behalf? What if the amount were significantly higher—would we still trust AI to complete the transaction?
This is the challenge of Agentic Payment: a world where AI no longer stops at searching and recommending, but can purchase products, access paid services, and make transactions to complete a task autonomously.
The important question is therefore no longer simply “Can AI make payments?” but “How much should we trust AI to transact on our behalf?”
Human-in-the-loop is safer—but it can become a bottleneck
This works well for an occasional purchase. But imagine an AI Agent handling 20 tasks on behalf of a company—purchasing data, calling APIs, or accessing paid services along the way.
If the Agent has to stop and wait for human approval every time a payment is required, can we really call the workflow fully automated? The AI may be capable of completing all 20 tasks on its own, but repeated human intervention becomes the bottleneck.
The challenge of Agentic Payment, therefore, is not about removing humans from the process entirely. It is about deciding when human approval is necessary—and when an AI Agent can be trusted to transact within predefined limits and guardrails.
Instead of giving AI our credit card, treat it like an employee
A company would not hand an employee a corporate credit card and simply say, “Buy whatever you think is appropriate.” There are spending limits, approved expense categories, authorized merchants, and records that allow every transaction to be reviewed later.
AI should be managed with the same principle.
If an organization wants an AI Agent to spend real money, the key is not only the payment technology itself, but the control layer built around it.
One starting point is to separate AI payments from human payments. This makes it easier to set spending limits and clearly identify what the Agent purchased, when the transaction happened, and how much was spent.
The next layer is to establish guardrails before the money leaves the account—for example, restricting purchases to approved merchants or categories, or setting spending limits over a defined period. Discovering that an AI made the wrong purchase after the transaction has already happened is merely damage control.
For higher-risk transactions, organizations may also need to separate the AI that takes an action from the AI that reviews it, similar to a Maker–Checker process or approval committee. The difference is that multiple AI Agents could potentially perform these layers of review within minutes.
A capable Agent, therefore, should not be measured only by whether it can complete a task, but also by what permissions it has, what guardrails govern its actions, and whether those actions can be audited.
If AI becomes the customer, will it be able to find your business?
There is another side of Agentic Payment that may be even more important: what happens when AI becomes the customer?
Most digital sales channels today are designed primarily for humans—from websites and mobile apps to social commerce and marketplaces.
But AI Agents do not navigate the digital world in the same way people do.
If product information, pricing, availability, or purchasing options sit inside systems an Agent cannot access, AI may not even know that a business exists—regardless of how good or competitively priced its products are.
Businesses whose information can be discovered, read, understood, and acted upon by AI may therefore gain an advantage in a world where Agents increasingly search, compare, and purchase on behalf of humans.
In the past, businesses invested in SEO so Google could find them. In the future, they may also need to ask: How do we make sure AI can find us, understand what we sell, and buy from us?
If AI can transact more frequently, do we still need to sell by the month?
Another potential shift is how products and services are priced.
One reason many services are sold through monthly subscriptions or packages is that every transaction carries operational costs—from payment processing to accounting and documentation.
If AI can automate more of this work, very small transactions—or nano payments—could become increasingly practical.
Instead of paying for a full month, customers could pay only for the time they actually use a service. Instead of purchasing a package, they might pay per use, per hour, or even per minute.
Agentic Payment could therefore change more than “who pays.” It could raise a much bigger question: “How should we pay for products and services in the first place?”
Before asking whether AI can pay, set the rules first
If organizations are considering allowing AI Agents to make transactions autonomously, the first step may not be choosing the payment technology. It is defining what AI is—and is not—allowed to do.
- Set clear spending boundaries: Give AI a defined budget, specify what it can purchase, and determine which transactions still require human approval.
- Build guardrails before money moves: Set permissions, approved merchants, categories, and risk-based spending limits to prevent mistakes before a transaction happens.
- Make every action auditable: Organizations should be able to understand what AI did, when it did it, what it spent money on, and why.
- Make your business AI-accessible: As AI increasingly searches, compares, and buys on behalf of humans, product information, pricing, and transaction channels need to be discoverable and accessible to Agents.
The next step for Agentic AI may not simply be making AI capable of doing more. It may be building the systems that allow us to trust it to do more—including spending real money.
Explore the full discussion on Agentic Payment in UPSTREAM Podcast EP.3 on the SCB 10X YouTube channel.
Article posted in Techsauce
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