The wave of change driven by artificial intelligence is quietly reshaping the foundations of the business world. As stated in "AI 2027" : "It is expected that after 2025, artificial intelligence will become more and more like an autonomous entity rather than a simple assistant." We are now at the turning point where intelligent agents are transforming from ordinary tools that cannot be relied upon to autonomous intelligent agents.

Readers who actively follow the world of AI and Fintech will find that since late 2024, Internet, payment, and e-commerce giants such as Paypal, Visa, Mastercard, Stripe, and Amazon have begun to deploy "intelligent business" and "intelligent payment". The logic behind this is actually very clear: the traditional business world has seen an increasingly clear trend, and the large-scale application of agentic interfaces will subvert all the original business logic and production relations based on the traditional GUI (Internet graphical user interface) for 30 years. On this basis, the traditional e-commerce operation, advertising marketing, and financial payment settlement logic have been completely rewritten, and even a new category will emerge: Agentic Commerce (Intelligence Commerce).

Most users and traders in the crypto world are still unaware of this paradigm shift, which is comparable to the shift from horse-drawn carriages to steam engines and from PC Internet to mobile Internet. This transformation of intelligent business is not just the intelligent extension of "e-Commerce" as many people know it.

This research report by OKX Ventures aims to provide readers (especially Crypto readers, because you actually have skin in the game!) with a panoramic perspective on intelligent business, systematically sort out its technical structure and path, analyze the commercial innovation of this change, and explore the core difficulties it faces in the final implementation process, and finally demonstrate why Crypto may become its indispensable underlying infrastructure.

1. What is Agentic Commerce?

We refer to the following companies' descriptions of Agentic Commcerce and Agentic Payment: Stripe (launched Stripe Issuing ), Visa (proposed the concept of Intelligent Commerce and its supporting API), Mastercard (launched Agent Pay ), and Coinbase (launched the x402 payment protocol ), and give the following simple definition:

Agentic Commerce is a business model driven by AI agents that can perform various tasks on behalf of users, including searching for products, comparing options, providing recommendations, and completing purchases. These AI agents are able to interact with e-commerce platforms, process transactions, and manage the entire shopping process, aiming to make the shopping experience more personalized, secure, and convenient. Amazon's "Buy for Me" feature (which allows AI agents to help users purchase goods from third-party brands) and OpenAI's "Operator" tool (which automatically completes online shopping tasks) are currently the most well-known examples.

Currently, Agentic Commerce is still an emerging field, and there is not much public business or commercial data. According to Gartner 's 2024 report, less than 1% of e-commerce companies or merchants have adopted Agentic AI into their company's business or services, but the market is very concerned about this technology. According to a 2025 e-commerce statistical survey , 90% of e-commerce companies are willing to learn how to integrate Agentic AI into their business.

Why did traditional payment giants collectively launch various new payment products adapted to intelligent scenarios last year and this year, even before Agentic Commerce was widely used? Did they see any huge opportunities behind it?

1.1 The role of human users has changed from "executors" to "clients", and key business decision-making links have been moved from the "checkout page" to the "intention layer"

Traditional online shopping is like visiting a well-designed virtual supermarket: consumers browse the shelves, compare products, and finally check out. The whole process revolves around "active exploration." The optimization goal of merchants is to make this process smooth and reduce any hesitation of users through exquisite interfaces, accurate recommendations, and fast payments.

Now, imagine a new world of Agentic Commerce: you don’t need to browse e-commerce websites one by one, compare price-performance ratios, or place orders manually. You only need to give a vague instruction to the AI assistant, such as "buy me a pair of running shoes." The AI will start immediately, searching countless merchants, screening products, analyzing prices, reviews and logistics, and even considering the environmental friendliness of the supply chain. During the whole process, you may not touch the screen once or enter a password once.

The key change is that the user's role has changed from "executor" to "client", and the core of commercial behavior has been upgraded from "click stream" to "intent stream". Consumption is no longer a series of discrete choices, but an overall authorization for the ultimate goal (human users can directly say to the AI assistant: I want to redecorate my house in the Mediterranean style, help me choose the materials).

When business decisions migrate from the "checkout page" to the "intention layer", the existing business system will face an avalanche of impact. From marketing to user growth strategies, the foundation of traditional e-commerce business logic based on human behavior analysis for decades has been overturned by the rational decision-making of AI agents:

A/B testing: AI can compare dozens of options in milliseconds, so it makes no sense to spend two weeks testing which button icon color has a higher conversion rate.

Personalized recommendations: All existing recommendation algorithms based on human browsing history are no longer valid, and the recommendation model needs to be reconstructed based on AI decision-making logic.

Shopping cart recovery: AI decision-making will not have the same "hesitation" or "abandonment" as humans due to various experiences or other subjective or objective reasons. Shopping cart abandonment rate and various corresponding optimization strategies will become history (the current global average shopping cart abandonment rate is 70% )

Traditional marketing relies on the "eyeball economy": beautiful pictures, sensational video ads, and red buttons for "limited time sale". These strategies to stimulate human impulse consumption all hide the merchants' intentions for human behavioral psychology. In contrast, AI will not be impulsive. It is an absolutely rational decision-making agent. It only pays attention to whether the data returned by the API is clear and whether the parameters are complete. It will coldly compare product specifications, historical prices, logistics timeliness, user reviews and even supply chain carbon footprints. From now on, there will be no more "user mind occupation".

In the future, Agentic Commerce marketing will no longer be about making eye-catching advertisements, but about creating a "machine-readable trust record". "Product-Agent Fit" will replace "Product-Market Fit". Whether your product can be easily indexed, understood and recommended by the mainstream AI intelligent ecosystem (such as MCP servers and A2A protocols) will determine its market survival.

However, before the intelligent agent can quickly make inferences and decisions based on human-commissioned goals and "output intentions" to move rapidly toward the ultimate goal: "commercial transaction completion", it will hit a solid wall and stop - the traditional payment system.

2. Fatal incompatibility: Why the traditional financial system is a speed bump for Agentic Commerce

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

The entire modern payment and risk control system is essentially an "anti-automation system." Its core design philosophy is: assuming that automation equals fraud.

Think about each link in our existing payment process:

CAPTCHA: Use a question that is difficult for machines to understand to prove that you are a "human".

SMS verification code/two-factor authentication (2FA): Assuming you have a physical device that can receive SMS and enter the verification code manually, this behavior is extremely difficult for the program to perform.

3D Secure: This forces you to go to a completely new banking page and enter your unique transaction password, completely disrupting any automated process.

Risk control behavior analysis: Advanced risk control systems will even analyze your mouse movement trajectory, typing speed, device fingerprints and other "human characteristics" to determine the authenticity of the transaction.

All these "safety measures" have become "shackles" in the era of Agentic Commerce: all kinds of questions equivalent to "Are you human?" block the autonomous intelligent agents we send out.

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

Agentic Payment belongs to this set of protocols. It belongs to the final payment settlement link in Agentic Commerce. AI agents use secure and efficient methods (such as tokenized credentials) to perform transactions on behalf of users. This ensures that the payment process is seamless and secure, and there are usually user-set restrictions and controls to maintain trust and security. Mastercard's "AgenticTokens" supports AI agents to complete subscriptions and recurring payments. PayPal's AgentToolkit helps AI agents handle payment processes. Visa and Stripe have similar tools. Stripe's recent experiment with Perplexity is a combination of the two. Users can use Perplexity as an interface to directly give instructions for your new home decoration to make comprehensive suggestions and provide specific products. When the user confirms that it is the solution they like, the Agent directly uses the Agent payment background built by Stripe to complete automated payment settlement and delivery.

I believe that you can roughly understand why giants such as VISA and Mastercard are eager to launch corresponding payment solutions adapted to Agentic Commerce. Because they are all betting on who will be the game maker to define the next generation of "machine-native" payment protocols. This is a bet on mastering the underlying infrastructure of the future business world, and the end point of this change is to return payment to its essence - the senseless flow of value.

3. What are the specific challenges in building a financial infrastructure that supports a smooth experience for Agentic Commerce? How to do it?

3.1 Core Challenges: Trust, Intent and Automation

The difficulty in building an agentic payment system is not a simple technical implementation, but a solution to the fundamental problem arising from the paradigm shift.

1. "Who can do it": From the challenge of traditional payment authentication to agentic commerce authorization

In the payment field, when we talk about end users, we usually focus on identity verification rather than authorization. If you click "buy" on an e-commerce website, you have explicitly given authorization and it is difficult to object (because you entered your credit card information and explicitly clicked the button), so the core of traditional payment is built around "identifying people", and its soul-searching question is: "How can I confirm that the operator is you?" - that is, identity verification.

However, in the future AI-driven business era, the payment field is about to undergo important changes: authorization is becoming a key link in the payment process, and the issue of authorization now seems more complex and interesting, because the user authorization instructions are not as clear as the simple "click the button to buy" scenario in traditional e-commerce. Human users can express their payment intentions in many ways. Another complex point is, when a payment request is made, who are we authorizing? Is it the human user, the agent, or the company that develops the agent?

The authorization issues we can think of in the intelligent payment scenario are:

Identity ghost: Should this "transaction requester" be the end human user, the AI model, the intelligent application developer, or the server that runs it? We lack a set of verifiable identity standards designed for "machines", which may lead to security vulnerabilities in every link

Authorization boundaries: How to safely delegate financial authority to an AI? How to accurately define and strictly enforce the boundaries of authorization (amount, time, merchant), and how to ensure that the authorization itself is not tampered with or abused are also new issues.

Responsibility: When an intelligent agent makes a mistake or is maliciously exploited to cause losses, who should bear the responsibility is a very tricky question. Unclear responsibilities are the biggest obstacle to large-scale applications.

2. What to do: The Intent Verification Gap

The problem of intent verification is actually a derivative of the authorization problem. The probabilistic nature of LLM is in natural conflict with the deterministic requirements of finance. Although the payment layer cannot repair the "illusion" of AI, a well-designed financial system must be able to bridge the gap between AI output and the user's true intention.

From instructions to intentions: Traditional payments process "payment instructions" (Pay 50 to Merchant X), which assumes that the instructions are accurate. However, intelligent payments need to process "transaction intentions" ("Buy me a medium oatmeal latte"). The payment system needs to be able to verify the final payment instructions with the original natural language intention.

AI behavior constraints: What we need is not a payment system that can understand AI's thoughts, but a system with strong "guardrails". It can constrain AI's behavior through structured data, strict rules at the API level, and even smart contract logic to ensure that its execution results are within the "safe zone" preset by the user. For example, a rule that only allows spending no more than $10 at Starbucks can effectively prevent high-value or erroneous transactions caused by AI "hallucinations".

3. Machine-native fund custody and payment settlement methods

As mentioned above, traditional payment systems are inherently “anti-automation”. All security measures designed for traditional GUIs will become shackles that hinder full automation in agent commerce scenarios. Therefore, we need a new set of payment APIs and settlement networks designed for machines, which may include the following features:

Programmatic-First: All interactions should be done through structured APIs rather than simulating human clicks on a GUI.

Frictionless Settlement: Transactions should be able to be completed with near-zero latency and cost, especially for microtransactions that are critical to supporting the machine economy.

Data Portability: Transactions should be able to carry rich, structured metadata for automated reconciliation, auditing, and building more advanced financial services, rather than just a simple transaction amount and merchant name like traditional payments.

3.2 The road to breakthrough: three stages towards autonomous finance

In the face of the above challenges, the industry is exploring an evolutionary path from "assistance" to "agent" and then to "autonomy". These three stages clearly show how far we are from a true intelligent economy.

Phase 1: Assisted Agents with Human Supervision

This is the most mainstream model at present. AI plays the role of an advanced "automatic form filling tool" responsible for all tedious front-end work, but "steps on the brakes" at critical moments and returns the final decision-making power to humans.

Implementation: The agent can complete all front-end tasks such as search, price comparison, and information filling, but it "stops" at the most critical payment link and returns control to humans. For example, it automatically fills in the credit card information, but requires you to manually enter the CVV code; or it guides you to the login page of PayPal or Stripe, and you complete the final authorization yourself.

Core technology: Essentially browser automation (simulating human actions) or pre-filling with stored credentials (such as ApplePay/Google Pay).

Core pain point: The biggest pain point is the fragmented experience. The efficiency improvement brought by the intelligent agent stops abruptly at the last step, and true "end-to-end automation" is not achieved.

Phase 2: Controlled Agent within the Scope of Authorization (Agent as a Proxy)

This is a battlefield that payment giants such as Visa, Mastercard, and Stripe are fighting fiercely. They are trying to create a "controlled digital wallet" for intelligent entities, with virtual cards and dedicated payment APIs at the core. Users can generate one or more virtual cards with strict restrictions for their own intelligent entities. Virtual cards can be set with single/total spending limits, specified merchant categories (such as only being used to pay for air tickets), and validity periods.

Implementation: The agent uses a dedicated API (such as Stripe’s Order Intent API) to call this virtual card to initiate payment. The entire process requires little user intervention. The cooperation between Perplexity Pro and Stripe is a typical case.

Technical core: Transform the trust relationship from "trusting an uncertain AI" to "trusting a payment tool with certain parameters and controlled by the card issuer." This is a clever risk transfer.

·Core pain points: We think this is the most suitable solution for large-scale applications because traditional merchants connected to the credit card system do not need to make any changes, and users do not have much perception of the changes in the entire process because they do not hold virtual cards. However, when the more agentic-native scenarios in intelligent business (such as B2B agentic business) grow to another level in the future, the programmability of authorization information and the limitation of bank cards in the amount of information data will become development constraints. In addition, some industry researchers have proposed that solutions similar to Stripe's virtual card are still essentially dependent on the traditional system of "manually inputting card information". Although it can be made "insensitive" by some workarounds such as screen capture or headless browser automated GUI interaction, these methods are still solutions with higher technical and compliance risks. In addition, Stripe's fee structure is not suitable for ultra-micro transactions (with a fixed transaction fee of $0.35 + an additional fee of 2.5%).

Let’s take the Agentic Commerce collaboration launched by Stripe and Perplexity this year as an example (users can directly instruct AI to find products in the Perplexity interface and complete payment purchases through the corresponding Agentic Payment service launched by Stripe).

Phase 1 (initial binding): The user goes through a standard Stripe Checkout process for the first time, not only completing the purchase, but more importantly authorizing the platform with their payment information and funds (settled to Stripe Balance). Phase 2 (subsequent contactless payment): This is the core process. When the user issues an instruction again, the platform (such as Perplexity) no longer disturbs the user, but directly requests a one-time virtual card with strict risk control rules (amount, merchant category, etc.) from Stripe Issuing (Stripe card issuance service). Stripe's role in the entire chain:

Stripe Checkout: Serves as the initial entry point to securely capture user payment information and authorization.

Stripe Balance: As the platform’s funding pool, all subsequent virtual card payments are paid from here.

Stripe Issuing: This is the “engine” of the entire process, generating controlled payment instruments (virtual cards) on demand, converting an uncertain AI behavior into a deterministic, controllable payment event.

Merchant verification mechanism (how do merchants verify that the AI is the user's AI): Merchants do not directly verify that "AI is you", but rely on Stripe's background verification. Stripe checks whether the virtual card is valid (within the limit, not expired); whether the transaction is initiated by the authorized platform; if the platform (such as Perplexity Pro) is hacked, Stripe's webhook and limit control can detect anomalies and suspend transactions.

Phase 3: Autonomous Economy with Native Wallet (Agent as an Economic Actor)

This is the ultimate form of Agentic Commerce and the stage where Crypto plays a core role. AI is no longer a vassal of human accounts, but a native digital economic participant with its own independent wallet and identity. It can autonomously participate in a brand new economic network designed for machines. We follow the brick thrown by Fintech Brainfood "Agents can live inside the stablecoin wallet" to imagine this technical solution, and its implementation is as follows:

Authorization — Rule Governance/Policy Layer: Humans authorize smart contracts Humans do not directly interact with AI to authorize. Instead, humans deploy a rule governance/policy smart contract that sets boundaries for AI’s execution behavior (e.g., when X, Y, and Z are met at the same time, the AI agent can use the funds)

·Core safe (TEE and other technologies): The AI’s decision-making brain and the AI’s native on-chain wallet private key are encapsulated in the hardware security black box of TEE, which is inaccessible to the outside world. TEE internal process: When the AI brain makes a payment decision, it will pass this decision to the wallet module inside TEE, and the wallet module will sign the transaction with the internal private key.

Transaction Execution & Settlement: Signed transactions are broadcast from the TEE and sent to the blockchain or interact with other AI agents (for example, merchants will have their own AI agents in the future).

4. Analysis of Agentic Payment Project, a Seed Player in the Financial Infrastructure of Future Smart Business

In the field of intelligent business, when we compare these projects, we can find that they have chosen different paths and focuses in building the future Agentic Payment infrastructure. In general, these innovations mainly revolve around three core issues: how to safely and effectively perform authorization, how to ensure that AI agents act within the boundaries set by users, and how to achieve payment settlement. The following is a brief analysis of the three projects Skyfire, Payman and Catena Labs:

Skyfire is committed to defining a set of standardized "authorization" protocols for Agentic Commerce. The team focuses more on prioritizing the application scenarios of future B2B agentic. They found that the biggest obstacle to autonomous B2B transactions (such as purchasing data and API calls) between AI agents in the future is the lack of a widely verifiable, machine-native identity. To this end, Skyfire's core product is a set of machine-native authorization protocols developed based on the traditional mature and open Internet identity authentication standards JWT/JWKS . The general technical logic of this set of protocols is: Skyfire, as a trusted centralized institution, issues an encrypted, time-limited and scope-limited "payment credential" to all Agents registered on its network. This credential can be independently and offline verified by any third-party service, thereby achieving efficient, low-cost, and programmable machine-to-machine business interactions without sacrificing security, thereby laying the foundation for the establishment of an open Agentic B2B network.

Unlike Skyfire, which focuses on inter-agent protocols, Payman chooses to focus on solving the pain point of "human instructions and control of agents", which is closer to the agent application layer, that is, the Intent Verification Gap problem mentioned above. The team clearly sees that with the explosion of AI applications, the most urgent need of developers is not a brand new transaction protocol, but a "financial capability layer" that can greatly simplify complex financial operations and can be safely embedded in any application. The core product created by Payman for this purpose is an abstract function payman.ask() and a set of complex financial intent definitions and financial transaction capabilities behind it. Its general technical logic is: through a powerful natural language interface, all "boring and dirty work" such as intent parsing, strategy execution, risk control, and bank docking are encapsulated, allowing developers to give their AI assistants or automation tools powerful financial capabilities through a line of natural language code. At present, the main application scenarios mentioned by the team include AI salary, automatic reimbursement, multi-agent approval, etc.

Catena Labs also focuses on solving the authorization problem of agentic commerce. The team is committed to building an open, compliant and crypto-native trust and transaction infrastructure for agentic economies. They believe that to completely solve the "identity crisis" and "transaction barriers" of AI agents, they cannot rely solely on credentials issued by centralized institutions, but must return to more basic, truly decentralized identity standards. To this end, Catena Labs' core product is an open source protocol framework called Agent Commerce Kit (ACK) (which deeply integrates W3C's decentralized identifiers and verifiable credentials), which mainly includes two sets of standards: identity layer (ACK-ID) and payment layer (ACK-Pay). What is slightly special is that their authorization layer solution is more decentralized in terms of authorization issuance rather than having authorization defined by a single institution.

5. Exploration in the Blue Ocean: Commercialization Model of Intelligent Business

Intelligent business will bring a structural impact to the e-commerce and search engine advertising industries, which have been the cornerstones of the Internet's business over the past 20 years. We need a new theoretical framework to understand this change, which can be called the "Intent Layer Theory".

In the past, value was generated in two places: the entry point for discovery (such as Google search) and the end point for transaction (such as Amazon's checkout page). Ben Thompson's "aggregation theory" perfectly explains how platforms that aggregate users (traffic) gain enormous pricing power. In the era of intelligent entities, the core of value is shifting upstream, from "discovery" and "transactions" to "expression and execution of intent." This new pricing power center is the "intention layer" - the interactive interface between users and their preferred AI assistants. All the original business logic and production relations based on the traditional GUI (Internet graphical user interface) for 30 years will be broken, and a new multi-level business ecosystem is emerging.

Companies that own mainstream AI assistants, such as OpenAI, have mastered the entrance to user intent and are the "gatekeepers" of the new business ecosystem. There have been many discussions on the market about the analysis of their business models. Transaction commission/affiliate marketing (Commission/Affiliate Model), premium feature subscription (Subscription Model), platform API-as-a-Product (API-as-a-Product) are their main commercialization paths. This is not the focus of this article and will not be elaborated on.

5.1 Challenges and opportunities for merchants and service providers in intelligent commerce

For companies that sell goods and provide services, the focus of competition will shift from "operating users" to "service machines", which will directly lead to the weakening of the direct connection between consumers and brands. In the future, loyalty may no longer be directed to a certain e-commerce platform or brand, but to the AI assistant that understands oneself best and the company behind it.

From "UX optimization" to "API (MCP) optimization": The focus of merchant competition is no longer the visual design or user experience of the website, but the "AI readability" of their products and services. Is your product data structured? Is the API stable, efficient, and well documented? Can pricing and inventory information be accurately obtained by AI in real time? These will become new moats. Merchants need to encapsulate their own business into a protocol that is easier for machines to read so that it can be discovered, compared, and called by AI agents (friends who are interested in MCP have had many related discussions this year).

Pay-for-Performance Bidding: Instead of buying keyword ads, merchants pay the intent-layer platform to become the "preferred supplier" or "certified supplier" for specific areas (such as "buying economy class tickets" or "booking Michelin restaurants"). Merchants only need to pay when the AI assistant adopts their solutions and generates actual sales or valid leads.

API-first Services: Companies can encapsulate their core business capabilities (such as logistics, design, content creation, and legal consulting) into standard APIs and sell them directly to other AI agents for invocation, becoming a “functional module” in the machine economy and being paid on a per-use or on-demand basis.

The business barriers of companies that have built "network effects" in the traditional business chain may be punctured, and the market will become more open. Stronger market driving forces will bring changes to business pricing models, and more dynamic and flexible pricing models will emerge. Intelligent experience will need clear cost-benefit information to make decisions, and the pricing model in the traditional business world will also be changed.

5.2 Business Path for Financial & Trust Infrastructure Providers

In Agentic Commerce, the primary challenge is authorization and intent verification before a transaction is initiated. On the one hand, the system must solve the "agent-to-agent" trust problem, that is, to provide a set of verifiable digital identities for AI agents to ensure the legitimacy and security of the interaction. Most of the current agentic payment companies are focusing on solving this problem. This may give rise to a business model similar to "AI identity authentication as a service", where service providers can charge for the issuance and verification of AI identity credentials, or provide enterprise-level AI identity management platforms to obtain subscription revenue. On the other hand, the system needs to bridge the "intention gap" between human ambiguous instructions and machine-executable financial operations, and safely convert natural language into precise API calls. This has given rise to the "Financial-Capability-as-a-Service" model, where service providers can encapsulate this capability into an API or SDK, charge according to the number of calls or transaction flows, or provide solutions for specific scenarios (such as AI automatic reimbursement).

In addition to authorization and intent verification, the last challenge is to build a payment settlement and trust infrastructure that is truly machine-native and supports automated transactions. Traditional financial tracks are designed for humans and face cost and efficiency bottlenecks when dealing with high-frequency, micro-amount, and programmable transactions driven by AI. In this regard, the technical solutions of each company will be different (whether it is a virtual card solution, a payment API, or allowing intelligent entities to have on-chain wallets), but their business logic may be consistent in terms of business models. The core is to charge for providing power and trust for automated value flows (transaction flow sharing, pay-per-use). In addition, providing value-added services may enhance the moat, such as providing programmable, dynamic risk control strategies for AI behaviors, automated compliance and audit tools, or cross-asset liquidity management solutions.

6. Ultimate Infrastructure: Why is Crypto the best partner for intelligent business?

6.1 The solutions of traditional payment companies such as Stripe and Visa may not be the final solution

As mentioned above, major U.S. payment companies are deploying solutions that are more suitable for intelligent entities and do not require major changes to existing financial facilities (we classify them as the second stage: controlled agents within the scope of authorization). The most representative one is the virtual card solution launched by Stripe.
Issuing, but these solutions are more like Middle Ground and are still far from the End Game's fully autonomous intelligent payment:

·Lack of native payment API interface and human-computer interaction design: There is currently no direct way to make payments through API calls. Although virtual cards have potential, they do not address this gap. Virtual card APIs support the issuance and management of virtual cards, but still rely on manual entry of card information (such as card number, expiration date, CVV) into the merchant's payment interface. Payment systems are essentially designed around human-centered interactions rather than programmatic execution. Relying on screen capture or headless browsers to simulate human operations is legally ambiguous and is questioned by the industry as being technically fragile, so this is not only a technical challenge but also a compliance challenge.

The prevalence of anti-automation and anti-fraud mechanisms: Most websites still use complex anti-bot systems such as verification codes to prevent automated interactions. These mechanisms make it difficult for agents or scripts to complete transactions, and human intervention is still required to bypass them. At the same time, automated transactions are often flagged by anti-fraud algorithms, which may result in payment rejections, account locks, or even closures.

·Compliance challenges with human-oriented compliance and ambiguous responsibility: Business processes and compliance systems established over decades are based on explicit human consent and responsibility. Every e-commerce or self-service API purchase is made through a graphical user interface designed for humans, which means that everyone needs to manually accept the purchase at the point of sale, including signing terms and conditions and completing certain business agreement processes. Automating these processes usually violates the website's terms of service. In addition, traditional PCI compliance will not like the behavior of saving credit card numbers (virtual cards are also credit cards) in smart proxy software because PCI has a set of strict management regulations for the processing, storage and transmission of card data.

In summary, the core challenge of solutions from traditional payment companies like Stripe and Visa is that traditional companies try to adapt machine behaviors to frameworks designed for humans and fail to fundamentally provide "machine-native" solutions.

6.2 Crypto provides native infrastructure for intelligent business

Cryptography, especially self-hosted wallets and public-private key systems, provides a set of "machine-native" solutions to Agentic Commerce's core challenges. First, it gives each AI agent an independent and verifiable digital identity through open standards such as decentralized identities (DIDs), fundamentally solving the "identity ghost" problem in traditional centralized systems. On this basis, authorization no longer relies on rigid and opaque background rules, but is upgraded to a programmable, sophisticated and completely transparent mechanism through smart contracts. Users can set authorization instructions for AI agents that cannot be tampered with, including multiple conditions such as specific amounts, merchant categories, and validity periods. This control granularity far exceeds traditional financial instruments and greatly reduces trust risks.

When payment is executed, an AI agent with an on-chain wallet can achieve truly automated and seamless transactions. It no longer needs to simulate human behavior to fill in credit card information, but interacts directly with the blockchain through APIs, which eliminates a lot of friction in the traditional payment process. Low-cost transactions driven by stablecoins make high-frequency, small payments between AIs economically possible, which is difficult to achieve with traditional payment tracks. More importantly, on the blockchain, payment and settlement are atomic operations that occur simultaneously, which not only eliminates the complex clearing and reconciliation processes in traditional finance, but also lays the foundation for real-time economic interactions between AI agents. All transactions are recorded on an unalterable chain, forming a publicly traceable audit trail, providing unprecedented transparency and trust for post-tracing and dispute resolution.

6.3 Implementation and Risks of AI Agents Having Crypto Wallets

When AI agents are given the ability to manage crypto wallets, a series of profound technical and operational risks arise. The most direct threat stems from attacks on the AI system itself, such as tampering with the AI agent or its operating environment, or even the direct theft of the private keys it manages. The core idea to address this challenge is to avoid letting the AI agent directly keep the complete private key, and instead adopt technologies such as MPC and TEE to distribute or programmatically control key management and transaction authorization, thereby eliminating single point risks. In addition, attacks may also occur in other links of the interaction chain, such as the user's authorization intent being tampered with during the transmission process, or the user's own wallet used for authorization being stolen. Therefore, it is crucial to establish a strong intent verification mechanism and introduce multi-factor authentication during key operations to build end-to-end security protection.

In addition to technical security challenges, a more fundamental obstacle comes from the ambiguity of law and regulation. The current legal framework designed for human actors makes it difficult to define the legal status of AI agents, resulting in a "liability vacuum." When a smart contract has a loophole, or an AI agent makes a wrong decision due to "hallucinations" and causes asset loss, the attribution of responsibility (users, AI developers, or platform parties) becomes a thorny issue. To achieve large-scale applications, Crypto's on-chain transparency and transaction traceability provide a solid data foundation for this, but the industry must work closely with regulators to explore new legal and compliance frameworks that adapt to the "machine-to-machine" economy.

7. More thoughts

If we start from first principles, what would it take to create a sovereign economic agent?

A sovereign identity: an identity that it owns and controls, rather than one that is issued and revoked by a centralized platform. This is what DIDs provide.

A sovereign wallet: the ability to hold and transfer value without the permission of an intermediary. This is what public-private key cryptography and self-custodial wallets provide.

A set of sovereign rules: The ability to operate under a set of transparent, inviolable, and programmably enforceable rules. This is what smart contracts provide.

Based on the above deduction, we can make predictions about the future:

Next 3-5 years: Stripe, Visa, and other solutions will dominate the early market

"Controlled agent" solutions (such as virtual cards) represented by Stripe and Visa will be successful in the short term. The reason is simple: they have unparalleled backward compatibility, and AI agents can immediately start transactions with millions of merchants around the world that already accept credit cards, without waiting for any transformation on the other side of the ecosystem. This solves the "chicken and egg" problem in the early market and can quickly convert AI's execution into commercial value. For most application developers who pursue rapid implementation, this is the option with the least resistance and the fastest results, but it may only be a transitional period before the early education market welcomes the arrival of a new business paradigm for the machine economy.

5+ years: The value of machine economy native solutions will become more prominent and reach a turning point

As the scale of Agentic Commerce grows exponentially, the "core pain point" of the second phase solution - the reliance on traditional card networks will become increasingly unbearable, and the many limitations of being unable to adapt to the new agentic native economy ( B2B agentic , A2A) will become prominent: the lack of programming authorization system (we spent a long time discussing how important the authorization issue is), the difficulty in building a portable agentic ID that supports sufficient identity information, high transaction fees (especially for trillions of microtransactions), and slow cross-border settlement will become a huge obstacle to the development of the entire intelligent economy. At that time, the market's attention will naturally turn to alternatives that are more naturally aligned with the machine native economy. The infrastructure provided by Crypto - stablecoins, smart contracts, decentralized identities, and verifiable credentials are no longer just a "better payment channel", but the only technical paradigm that can provide the necessary "sovereignty" for a truly autonomous economy.

Disclaimer:

This article is for reference only. This article only represents the author's views and does not represent the position of OKX. This article is not intended to provide (i) investment advice or investment recommendations; (ii) an offer or solicitation to buy, sell or hold digital assets; (iii) financial, accounting, legal or tax advice. We do not guarantee the accuracy, completeness or usefulness of such information. Holding digital assets (including stablecoins and NFTs) involves high risks and may fluctuate significantly. You should carefully consider whether trading or holding digital assets is suitable for you based on your financial situation. Please consult your legal/tax/investment professionals for your specific situation. Please be responsible for understanding and complying with local applicable laws and regulations.

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