Tiger Research: How Crypto Giants Are Betting on AI Agent Payment Infrastructure

This report is authored by Tiger Research. To achieve true autonomous automation, native payment capabilities are essential. The market has already begun actively preparing for this shift.

Key Points

• The primary payment actors are shifting from humans to AI Agents, making payment infrastructure a core requirement for true autonomy.
• Big tech companies (including Google AP2 and OpenAI Delegated Payment) are designing approval-based automated payment systems built on existing platform infrastructure.
• Cryptocurrencies, through standards like ERC-8004 and x402, leverage NFT-based identity verification and smart contracts to enable decentralized payment models.
• Big tech companies prioritize convenience and consumer protection, while cryptocurrencies emphasize user sovereignty and broader agent-level execution capabilities.
• The key question for the future is: Will payments be controlled by platforms or executed via open protocols?

1. Payments Are No Longer Exclusive to Humans

Source: macstories (Federico Viticci)

Recently, “OpenClaw” has garnered widespread attention. Unlike major AI systems like ChatGPT or Gemini, which primarily focus on retrieving and organizing information, OpenClaw allows AI Agents to execute tasks directly on the user’s local PC or server.

Through messaging platforms like WhatsApp, Telegram, and Slack, users can issue commands, and the Agent will autonomously perform tasks such as managing emails, coordinating calendars, and browsing web pages.

Since it operates as open-source software and is not tied to any specific platform, OpenClaw functions more like a personal AI assistant. Its architecture is favored for its flexibility and user-level control.

However, a key limitation remains. For AI Agents to be fully autonomous, they must be able to perform payments. Currently, Agents can search for products, compare options, and add items to carts, but final payment authorization still requires human approval.

Historically, payment systems have been designed around human actors. In environments driven by AI Agents, this assumption no longer holds. For automation to be truly autonomous, Agents must be able to independently evaluate, authorize, and complete transactions within defined constraints.

Anticipating this shift, major tech firms and native crypto projects have launched 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 initiatives, Google is currently the only large company with a structured implementation framework.

AP2 divides the transaction process into three authorization layers (Mandate Layers). This structure allows for independent monitoring and auditing at each stage.

• Intent Mandate: Records the user’s desired action.
• Cart Mandate: Defines how to execute the purchase under preset rules.
• Payment Mandate: Executes the actual transfer of funds.

Example: Suppose Ekko instructs Google’s AI Agent to “find and buy a winter jacket under $200” on Google Shopping.

• Intent Mandate: Ekko’s command to the AI agent to purchase “a winter jacket with a maximum budget of $200.” This information is recorded as a digital contract on-chain, representing the intent.
• Cart Mandate: The AI agent follows the intent, searches partner merchants for matching items, and adds suitable products to the cart. It verifies the price (e.g., $199, within budget ✓) and confirms the shipping address.
• Payment Mandate: Ekko reviews the selected item and approves. The $199 is processed via Google Pay. Alternatively, the AI agent can automatically complete the payment within preset parameters.

Throughout this process, the user does not need to input additional information. Google AP2 relies on existing user credentials (pre-registered cards and addresses), reducing barriers to entry and simplifying adoption.

Source: Google

However, Google currently only supports agent payments within its partner network, limiting usage to a controlled ecosystem, which restricts broader interoperability and open access.

3. Cryptocurrencies: Self-Hosting and Open Exchange

The crypto space is also developing payment infrastructure for AI Agents, but with a markedly different approach from big tech. While large platforms build trust within controlled ecosystems, the crypto domain begins with a different question: Can AI Agents gain trust without relying on centralized platforms?

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

First is the identity layer. For AI Agents operating on blockchain, being recognizable is essential. ERC-8004 serves this purpose. It issues NFTs that are not art collectibles but credential NFTs containing structured identity data. Each token includes three components:

1. Identity
2. Reputation
3. Validation

These elements together form a verifiable on-chain identity certificate.

In terms of payment mechanisms, x402 functions as the payment pathway. Developed by Coinbase, x402 is a crypto-native payment standard for AI Agents. It enables Agents to conduct autonomous transactions using stablecoins. Its core feature is automated smart contract execution, with embedded conditional logic. Once conditions are met, settlement occurs without human intervention.

When ERC-8004 (identity) and x402 (payment) are combined, AI Agents can verify counterparties and execute transactions without relying on centralized platforms.

Example: Ekko instructs his Agent A to purchase a used laptop with a maximum budget of $800. Seller’s Agent B communicates directly.

• Mutual verification: Using ERC-8004 NFTs, verify identity and reputation scores (e.g., reputation 72, balance confirmed).
• Escrow smart contract: Transfer $800 from wallet into escrow, locking funds until delivery is confirmed.
• Settlement and reputation update: After transaction completion, x402 automatically settles, and both parties’ reputation records are updated and written into their respective ERC-8004 NFTs.

No intermediaries are involved. The two AI Agents conduct the transaction directly via blockchain-based verification and settlement, exemplifying a crypto-native agent-to-agent (A2A) business model.

4. Big Tech vs. Cryptocurrency: Differences in AI Agent Operation

Google AP2 exemplifies a controlled model designed for verified partners. Google restricts market participation to protect consumers. Because AI Agent execution results are probabilistic rather than deterministic, if a transaction error occurs, liability may ultimately fall on the payment infrastructure provider. To reduce failure risk, Google has an incentive to shrink its ecosystem.

This restricted ecosystem enhances stability but also limits the ability of agents to operate autonomously and optimize choices across broader markets.

In contrast, ERC-8004 and x402 reflect a more open architecture. The crypto model aims for permissionless and interoperable systems.

While end-to-end execution is not yet perfect, the long-term vision is for Agents to independently manage daily consumption. Major platforms might attempt to integrate key retail channels, but open crypto standards have structural advantages in handling small-value, high-frequency programmable payments (microtransactions). For example, an Agent purchasing 1000 images at $0.01 each via a crypto-native path offers higher operational efficiency.

Of course, the absence of centralized entities also entails trade-offs: Identity verification standards must be established in a decentralized manner, and no single entity bears ultimate responsibility for failures.

Summary

Both big tech companies and the crypto space are pursuing the same goal: enabling autonomous AI Agent commerce. The difference lies in architecture: big tech favors closed, controlled systems, while crypto advocates open, protocol-based models.

The future is more likely to involve interoperability between these approaches rather than a zero-sum competition.