AI-driven transformation is reshaping the business world

The wave of transformation driven by artificial intelligence is quietly reshaping the foundations of the business world. It is expected that after 2025, artificial intelligence will increasingly resemble autonomous entities rather than mere assistants, and we are at the turning point of the evolution of agents from ordinary tools to autonomous intelligences.

Starting from the second half of 2023, many internet, payment, and e-commerce giants have begun to lay out plans for "agentic commerce" and "agentic payment". The logic behind this is very clear: the traditional business world has seen an increasingly clear trend, namely that the large-scale application of agentic interfaces will disrupt all business logic and production relationships that have been established over the past 30 years on traditional GUIs. On this basis, traditional e-commerce operations, advertising marketing, and financial payment settlement logic will be completely rewritten, and even new categories may emerge: Agentic Commerce ( Intelligent Commerce ).

This transformation of agent-based commerce is far more than just an intelligent extension of e-commerce. This article aims to provide readers with a panoramic view of agent-based commerce, systematically outlining its technical structure and pathways, analyzing the commercial innovation of this transformation, and discussing the core difficulties faced during the final implementation process, ultimately arguing why cryptocurrency technology may become its indispensable underlying infrastructure.

1. What is Agentic Commerce?

Agentic Commerce is a business model driven by AI agents, which can perform various tasks on behalf of users, including searching for products, comparing options, providing recommendations, and completing purchases. These AI agents are capable of interacting with e-commerce platforms, processing transactions, and managing the entire shopping process, aimed at making the shopping experience more personalized, secure, and convenient. The "Buy for Me" feature of a major e-commerce platform ( allows AI agents to assist users in purchasing goods from third-party brands ), and the "Operator" tool of an AI company ( automatically completes online shopping tasks ), which are currently the most well-known examples.

Currently, Agentic Commerce is still an emerging field, and there is not much publicly available business or commercial data. According to a report from a research institution in 2024, currently, less than 1% of e-commerce industry enterprises or merchants have adopted agentic AI into their company operations or services. However, there is a high level of market interest in this technology. According to a 2025 e-commerce statistical survey, 90% of e-commerce businesses are willing to learn how to integrate agentic AI into their operations.

So why did traditional payment giants collectively launch various new adaptive agentic commerce payment products aggressively last year and this year, even before agentic commerce has been widely applied? Did they see some huge opportunities behind it?

1.1 The role of human users has shifted from "executor" to "client", and the key business decision-making process has been moved from the "checkout page" to the "intention layer".

Traditional online shopping is like walking through a meticulously designed virtual supermarket: consumers browse the shelves, compare products, and finally check out, with the entire process revolving around "active exploration." The merchants' optimization goal is to make this process as smooth as possible, reducing any hesitation from users through beautiful interfaces, precise recommendations, and fast payments.

Now, imagine a new world of Agentic Commerce: you no longer need to browse e-commerce websites one by one, compare price-performance ratios, or place orders manually. You just need to give a vague command to your AI assistant, such as "Help me buy a pair of running shoes." The AI immediately activates, searches countless merchants, filters products, analyzes prices, reviews, and logistics, and even considers the environmental impact of the supply chain. Throughout the entire process, you might not have touched the screen once or entered a password.

The key transformation is: the role of users has shifted from "executor" to "trustee", and the core of commercial behavior has upgraded from "click stream" to "intention stream". Consumption is no longer a series of discrete choices, but a holistic authorization towards the final goal. Human users can directly tell the AI assistant: I want to redecorate my house in a Mediterranean style, help me choose materials.

When business decisions shift from the "checkout page" to the "intention layer", the existing business system will face a snowball impact. From marketing to user growth strategies, the traditional e-commerce business logic, which has been based on human behavior analysis for decades, is being disrupted by the rational decisions of AI agents.

• A/B Testing: AI can compare dozens of options in milliseconds, making two weeks of testing which button icon color has a higher conversion rate meaningless.

• Personalized Recommendations: All existing recommendation algorithms based on human browsing history are invalid, and the recommendation model needs to be reconstructed based on AI decision-making logic.

• Shopping Cart Recovery: AI decision-making will not have the "hesitation" or "abandonment" that humans experience due to various subjective or objective reasons. The shopping cart abandonment rate and various corresponding optimization strategies will become a thing of the past. Currently, the global average shopping cart abandonment rate is 70%.

Traditional marketing relies on the "attention economy": exquisite images, emotional video ads, and the red button for "limited-time offers". These strategies, aimed at triggering impulsive consumption in humans, are all cleverly designed based on the psychology of human behavior. In contrast, AI does not have impulses; it is an absolutely rational decision-making agent. It only focuses on whether the data returned by the API is clear, whether the parameters are complete, and it will coldly compare product specifications, historical prices, logistics efficiency, user reviews, and even the carbon footprint of the supply chain, eliminating the need for "capturing user mindsets".

The future of Agentic Commerce marketing is no longer about creating eye-catching advertisements, but about building "machine-readable trust records." "Product-Agent Fit" will replace "Product-Market Fit." Whether your product can be easily indexed, understood, and recommended by mainstream AI agent ecosystems like MCP servers and A2A protocols ( will determine its market survival.

However, before the intelligent agent quickly advances towards the final goal of "completing commercial transactions" by reasoning and decision-making with human delegated objectives and "producing intents", it will hit a hard wall and stop — the traditional payment system.

2. Fatal Incompatibility: Why Traditional Financial Systems are the Speed Bumps of Agentic Commerce

Intelligent agents can perfectly perform information collection, analysis, and decision-making, but when they reach the final link of the business closed loop, they will hit a hard wall, which is the financial payment system that we have spent decades building, designed entirely for humans.

The entire modern payment and risk control system is essentially a "counter-automation system." Its core design philosophy is: assume that automation equals fraud.

Think about every aspect of our current payment process:

• Graphic verification code: Use a problem that is difficult for a machine to recognize to prove that you are "human".

• SMS verification code/two-factor authentication: Assuming you have a physical device that can receive SMS messages and can manually enter the verification code, this action is extremely difficult for the program.

• 3D Secure Authentication: It will force a redirect to a brand new bank page, requiring you to enter a separate transaction password, which completely interrupts any automated process.

• Risk Control Behavior Analysis: Advanced risk control systems may even analyze your mouse movement trajectory, typing speed, device fingerprints, and other "human characteristics" to determine the authenticity of transactions.

All these "security measures" have become "shackles" in the era of Agentic Commerce: various inquiries equivalent to "Are you human?" obstruct our dispatched autonomous agents.

Therefore, the future of payment is no longer a "checkout page", but must be a "protocol". This is a revolution about trust and authorization mechanisms. We need a brand new digital credential system that allows users to securely issue a "programmable authorization" to their AI agents, with clear scope, time limits, and amount restrictions.

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Agentic Payment belongs to this protocol, which is part of the final payment settlement phase in Agentic Commerce. AI agents use secure and efficient methods ) such as tokenized credentials ( to represent users in executing transactions. This ensures that the payment process is seamless and secure, usually with user-defined limits and controls to maintain trust and security. "Agentic Tokens" from a certain payment company support AI agents in completing subscriptions and recurring payments, while the Agent Toolkit from a certain payment platform assists AI agents in handling payment processes. Many payment giants have similar tools. A recent experiment conducted by a certain company in collaboration with a certain AI company is a combination of these two, allowing users to directly give commands through the AI interface to receive comprehensive suggestions for decorating their new home and provide specific products. Once users confirm that the proposed plan is to their liking, the agent directly uses the payment backend set up by that company to complete automated payment settlement and delivery.

I believe that by now you can roughly understand why many payment giants are eager to launch corresponding payment solutions that are compatible with Agentic Commerce. Because they are all betting on who will be the rule maker of the next generation "machine-native" payment protocol, this is a gamble to control the underlying infrastructure of the future business world, and the endpoint of this transformation is to return payments to their essence – the seamless flow of value.

3. What are the specific challenges in building a financial infrastructure that supports a seamless experience for Agentic Commerce? How can this be achieved?

) 3.1 Core Challenges: Trust, Intent, and Automation

The dilemma of building the Agentic Payment system is not simply a technical implementation, but rather a fundamental problem arising from paradigm shifts.

1. "Who Can Do It": From the identity verification of traditional payments to the challenges of Agentic Commerce agent authorization.

In the payment field, when it comes to end users, we usually focus on authentication rather than authorization. If you click "Buy" on an e-commerce site, you are clearly giving authorization, and it is hard to dispute ( because you entered your credit card information and clearly clicked the button ), so the core of traditional payment is built around "identifying people"; its soul-searching question is: "How do I confirm that the operator is you?" — that is authentication.

But in the future business era driven by AI agents, significant changes are about to occur in the payment field: authorization is becoming a key link in the payment process, and this issue of authorization now seems more complex and interesting, because the instructions for user authorization are not as clear as the traditional e-commerce scenario of "clicking a button to buy," as human users can express their payment intentions in various ways. Another complicated point is, when a payment request is made, exactly who are we authorizing? Is it the human user, the agent, or the company that developed the agent?

The issue of authorization in the current payment scenarios we can think of involving intelligent agents:

• Identity Ghost: Should this "transaction requester" be a terminal human user, an AI model, an agent application developer, or the server running it? We lack a set of verifiable identity standards designed for "machines," which could lead to security vulnerabilities at every stage.

• Authorization Boundaries: How to securely delegate financial permissions to an AI? The boundaries of authorization ( amount, time, merchant ) how to be precisely defined and strictly enforced, and how to ensure that the authorization itself is not tampered with or misused is also a new problem.

• Liability Attribution: When an agent makes a mistake or is maliciously exploited causing damage, determining who is responsible is a very tricky issue. Ambiguous rights and responsibilities are the biggest obstacles to large-scale application.

2. "What to do": Intent Verification Gap

The issue of intent verification is actually a derivative of authorization issues, and there is an inherent contradiction between the probabilistic nature of LLM and the deterministic requirements of finance. Although the payment layer cannot fix the "hallucination" of AI, a well-designed financial system must be able to bridge the gap between AI outputs and users' true intentions.

• From command to intention: Traditional payment processes a "payment instruction" ### Pay $50 to Merchant X (, assuming this instruction is accurate. In contrast, agent payments need to handle a "transaction intention" ) "Help me buy a medium oat latte" (.