The Year of AI Payments: When Your Agent Learns to Pay on Its Own

Source: Tiger Research

Author: Ekko, Ryan Yoon

Original Title: AI Agent Payment Infrastructure: The Direction of Crypto and Big Tech

Compiled and Arranged: BitpushNews

An era driven by AI and led by automation is approaching. For automation to be truly "autonomous," it must possess native payment capabilities. The market has already begun to position itself for this shift.

Core Points

• The payer is shifting from humans to AI Agents, making payment infrastructure a core requirement for achieving true autonomy.

• Big Tech companies (including Google AP2 and OpenAI delegated payments) are designing approval-based automated payment systems on top of existing platform infrastructure.

• Cryptocurrency (via ERC-8004 and x402) utilizes NFT-based identity and smart contracts to enable intermediary-free payment models.

• Big Tech prioritizes convenience and consumer protection, while cryptocurrency emphasizes user sovereignty and broader Agent-level execution capabilities.

• The key future question is: will payments be controlled by platforms or executed by open protocols.

1. Payments Are No Longer Exclusive to Humans

Source: macstories (Provided by Federico Viticci)

Recently, "OpenClaw" has garnered widespread attention. Unlike AI systems like ChatGPT or Gemini, which primarily retrieve and organize information, OpenClaw enables AI Agents to execute tasks directly on the user's local PC or server.

Through instant messaging platforms like WhatsApp, Telegram, and Slack, users can issue commands, and the Agent autonomously executes tasks including email management, calendar coordination, and web browsing.

As it runs as open-source software and is not tied to a specific platform, OpenClaw functions more like a private AI assistant. This architecture is favored for its flexibility and user-level control.

However, a key limitation remains: for AI Agents to achieve full autonomy, they must be able to execute payments. Currently, Agents can search for products, compare options, and add items to the cart, but the final payment authorization still requires human approval.

Historically, payment systems were designed around human actors. In an AI Agent-driven environment, this assumption no longer holds. If automation is to become fully autonomous, Agents must be able to independently evaluate, authorize, and complete transactions within defined constraints.

Anticipating this shift, major tech giants and crypto-native projects have introduced technical frameworks over the past year aimed at enabling Agent-level payments.

2. Big Tech: Building Agent Payments on Existing Infrastructure

In January 2025, Google launched AP2 (Agent Payment Protocol 2.0), expanding its AI Agent payment infrastructure. While OpenAI and Amazon have also outlined related plans, Google is currently the only major company with a structured implementation framework.

AP2 divides the transaction process into three Mandate Layers. This structure allows for independent monitoring and auditing of each stage:

1. Intent Mandate: Records what the user wants to do.

2. Cart Mandate: Defines how the purchase is executed based on preset rules.

3. Payment Mandate: Executes the actual fund transfer.

Example: How Google AP2 Operates

Suppose Ekko asks the AI Agent on Google Shopping to "find and buy a winter jacket under $200".

• Intent Mandate: Ekko instructs the AI Agent to buy "one winter jacket, maximum budget $200". This information is recorded on-chain as a digital contract, known as the Intent Mandate.

• Cart Mandate: The AI Agent follows the stated intent, searches partner merchants for products matching "one winter jacket" and "maximum budget $200", and adds eligible items to the cart.

  "Selected item: Winter jacket", "Price verification: $199 (Meets budget ✓)"

  "Added to cart", "Shipping address confirmed".

• Payment Mandate: Ekko confirms the item selected by the AI Agent and clicks the payment approval button. The $199 is processed via Google Pay. Alternatively, the AI Agent can also complete the payment automatically within predefined parameters.

Throughout the process, the user does not need to input additional information. In the case of Google AP2, the system runs on top of Google Pay and utilizes pre-registered card details and shipping addresses. Because AP2 relies on existing user credentials, it reduces onboarding friction and simplifies the adoption process.

Source: Google

However, Google currently only supports Agent-based payments for companies within its partner network. Therefore, its usage remains confined to a controlled ecosystem, limiting broader interoperability and open access.

3. Cryptocurrency: Self-Custody and Open Exchange

The crypto space is also developing payment infrastructure for AI Agents, but the approach differs from Big Tech. While large platforms build trust within controlled ecosystems, the crypto space starts with a different question: can AI Agents be trusted without relying on centralized platforms?

Two core standards aim to achieve this: Ethereum's ERC-8004 and Coinbase's x402.

Combining Identity and Payment

First, consider the identity layer. Just as humans need IDs to access digital services, AI Agents operating on blockchain networks must be identifiable. ERC-8004 serves this function.

It is issued in the form of an NFT, but not as a media collectible; rather, it is a credential NFT containing structured identity data. Each token consists of three components:

1. Identity

2. Reputation

3. Validation

These elements together form a verifiable on-chain identity certificate. In e-commerce, participants review ratings and transaction history before transacting; the same logic applies to AI Agents. ERC-8004 provides Agents with verifiable credentials, allowing other Agents to assess the suitability of a transaction based on transparent data.

However, identity alone does not enable value transfer; a payment mechanism is also needed. This role is filled by x402.

If ERC-8004 is the digital ID card, then x402 is the payment rail. Developed by Coinbase, x402 is a crypto-native payment standard for AI Agents. It enables Agents to conduct autonomous transactions using stablecoins.

Its core function is automated smart contract execution. Conditional logic, such as "automatically transfer funds after predefined criteria are met," is embedded directly in the code. Once conditions are satisfied, settlement occurs without human intervention.

When ERC-8004 for identity is combined with x402 for payment, AI Agents can verify counterparties and execute transactions without relying on centralized platforms. Trust and settlement are handled at the protocol level, not through platform control.

Example Scenario: Agent-to-Agent Commerce with ERC-8004 and x402

Assume a near-future AI Agent environment: Ekko instructs his AI Agent (Agent A) to buy a used laptop with a maximum budget of $800. The marketplace runs its own AI Agent (Agent B), which communicates directly with Ekko's Agent to execute the transaction.

1. Mutual Verification:

   Before the transaction, both Agents verify each other's credentials and confirm the product meets specific requirements.

• Identity Check: Verified via ERC-8004 NFT

• Ekko's Agent: Reputation score 72, confirmed balance $800

• Seller's Agent: Reputation score 70, confirmed eligible laptop stock

• Result: Both Agents are approved for the transaction.

• Escrow: $800 is transferred from Ekko's Agent wallet to a smart contract.

• Conditional Lock: Funds remain locked until delivery is confirmed.

• Release: Upon confirmation of delivery, the $800 is automatically transferred to the seller.

• Ekko's Agent: Reputation 72 → 80 (+5 fast delivery, +3 description match)

• Seller's Agent: Reputation 70 → 78 (+5 fast delivery, +3 description match)

• The updated evaluation records are written into each Agent's ERC-8004 NFT.

Throughout this process, no intermediaries are involved, and no platform approval is needed. The two AI Agents transact directly through blockchain-based verification and settlement. This reflects the crypto-native model of Agent-to-Agent commerce.

4. Big Tech vs. Cryptocurrency: Differences in the AI Agent Operating Domain

Control vs. Openness

Google AP2 represents a controlled model designed for approved partners.

Google limits market participation to vetted merchants, citing consumer protection. Even with a structured mandate framework, Agent behavior cannot be fully guaranteed. Unlike deterministic systems where inputs and outputs directly match, AI Agent execution produces probabilistic outcomes.

If an Agent connects to an unreliable partner and a transaction error occurs, liability could ultimately fall on the payment infrastructure provider. To reduce the probability of failure by even 0.01%, Google is incentivized to narrow its ecosystem. This restricted ecosystem enhances stability and manageability but may limit the Agent's ability to operate autonomously across a broader market and optimize among multiple options.

In contrast, ERC-8004 and x402 reflect a more open architecture. The crypto model aims for permissionlessness and interoperability, rather than being tied to a platform.

Efficiency and Use Cases

AI Agents are still in the early stages of development. End-to-end execution, from complex requests to autonomous payments, is not yet seamless. However, the anticipated long-term scenario is Agents independently managing daily consumption. For example, a user might instruct an Agent to restock groceries, and the Agent would assess inventory gaps and automatically complete the purchase.

Large platforms may attempt to aggregate major retail channels to support this model within a unified environment. This approach could enable reliable everyday use cases within a controlled framework. However, closed ecosystems face structural limitations in integrating all potential counterparties, including small online merchants, independent websites, decentralized finance protocols, and trading venues.

Furthermore, if digital content increasingly shifts to paid access models, Agents may need to execute high-frequency micropayments. Open crypto standards may have a structural advantage. For example, an AI Agent could buy 1,000 creator-generated images at $0.01 per unit or pay $1 to access a research article. For small, programmable payments, crypto-native rails may offer higher operational efficiency.

That said, the lack of a central authority also brings trade-offs. Identity evaluation standards must be established in a decentralized manner, with no single entity bearing ultimate responsibility for failure. Balancing openness with accountability remains a key design challenge, which will depend on technological maturity and improved ease of use.

Summary

Big Tech and the crypto space are pursuing the same goal: enabling autonomous AI Agent commerce. The difference lies in the architecture. Big Tech favors closed, controlled systems, while the crypto space promotes open, protocol-based models.

This is not a zero-sum game; a more likely trajectory is interoperability between the two approaches. At the current stage of technological advancement, ongoing development must prioritize reliability and user experience.

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