Author: Kevin, Movemaker Researcher; Source: X, @MovemakerCN. Google's recently released AP2 protocol defines the underlying rules for payments and transactions in the upcoming agent economy. Its core mission is to complete the final piece of the puzzle—value settlement—between "AI analysis" (based on MPC) and "agent execution" (based on A2A). It aims to standardize payment processes and interaction rules for AI agents, ultimately fostering AI agents capable of autonomously completing commercial activities on behalf of users. AP2's primary application scenarios target core areas of the traditional internet, such as e-commerce and subscription services. This means that its original design objective was to solve the problem of securely delegating payment authorization from humans to AI within the existing business framework. This will directly impact the direction of e-commerce operations, freeing users from clicking "buy" and "subscribe" to instead delegate their intent and set rules to AI agents. Based on this logic, we need to more carefully assess AP2's impact on the crypto industry. It wasn't designed for Web3 native scenarios, but rather to incorporate crypto payments (especially stablecoins) as an optional, integrated payment option within its broader framework. Therefore, rather than saying AP2 will trigger an AI boom, it's more accurate to say that it provides an interface for crypto assets to enter mainstream AI applications. Understanding AP2's Predecessor Protocols: MCP and A2A AP2's existence is built on the development paths of its two predecessor protocols, MCP and A2A. Understanding the nature and limitations of these two protocols is key to understanding the true purpose of AP2. MCP solves the problem of connecting AI with tools, while A2A attempts to address the problem of collaboration between AI and other AI. What is MCP? Bringing AI into Truly Connected with the Real World Large language models (such as Gemini and GPT) are essentially "super brains" isolated from the real world. They possess vast amounts of static knowledge, but lack access to real-time information and cannot directly operate any external software. A user's request, such as "Book me a flight to Osaka tomorrow," is impossible because it lacks the ability to interact with booking websites. The Model Context Protocol (MCP) was proposed to address the problem of how AI communicates with the external environment. MCP defines a standardized communication format that enables AI to make requests to external tools (such as databases and APIs) and receive responses. Think of it like installing a telephone in this "secret room brain." The Model Context Protocol (MCP) is a "standard intercom system" designed to break this closed room. Promoted by industry giants like Anthropic, it aims to address the problem of standardized AI interaction with external data and tools (such as databases, software, and blockchains). Through this "intercom," AI models can issue requests to the outside world using a standard language. The MCP system accurately relays these requests to the corresponding external tools (such as weather APIs or ticket booking website APIs), and once the tools complete the process, returns the results to the AI in a standardized format. However, analyzing current MCP implementations reveals that it's more like an "intercom" that can only dial a few preset numbers. Currently, AI's ability to call external tools is highly dependent on the platform's vertical integration. For example, when searching, Gemini defaults to and exclusively calls Google Search; its code execution also runs within the platform's own sandbox environment. This model has direct impacts on users and developers: For users, the upper limit of AI capabilities is locked by the platform. Users cannot choose third-party tools they deem superior (for example, using Perplexity for AI search or calling specific financial data APIs). This significantly limits the practicality of AI, and the user experience is bound to the platform's ecosystem. For developers, this creates a de facto market barrier. Even if developers create AI applications that far surpass the platform's native tools in a specific field, they cannot be fairly discovered and called upon by mainstream AI agents. Innovation is stifled, and market competition is confined to a closed, platform-dominated collaborative system. A2A: From "Internal Phone" to "Public Yellow Pages" If MCP gives AI the "hands and feet" to operate tools, then the A2A (Agent-to-Agent) protocol teaches independent AI agents how to "communicate and collaborate." Built on MCP, it defines a set of common rules that enable independent AI agents developed by different companies and developers to discover and understand each other's capabilities, and collaborate to complete complex tasks that would be beyond the capabilities of a single agent. The significance of A2A lies in upgrading MCP's task processing model from "single-soldier operations" to "project subcontracting." For example, when a user requests a complex plan like "planning a beach party in Hawaii next month," in a closed system with only MCP, individual agents can only clumsily access the limited tools available on the platform. However, in A2A's vision, individual agents can act as "contractors." They break down the task into smaller pieces, then post the request within the A2A network, seeking out professional "travel agents," "catering agents," and "event planning agents" to collaborate on the task. This paradigm shift will have profound implications: For users: The capabilities of AI agents will increase exponentially. Users can confidently delegate extremely complex, cross-disciplinary tasks because AI is no longer simply a tool operator, but a resource coordinator. For the industry: A2A aims to create an open, interoperable agent service marketplace. It addresses three core issues: discovery (establishing a mechanism similar to an agent app store), understanding (a standardized description language for agent capabilities), and collaboration (standardizing the process of task allocation and progress synchronization). Once this system matures, any agent developer who meets the standards will be able to register their services and be fairly selected by the market. AP2's Core Mechanism: Establishing a Trust and Authorization Framework for Agent Transactions The MCP and A2A protocols address the issues of "what agents can do" and "how they collaborate." However, this immediately raises a more thorny business challenge: when agents begin to independently perform financial operations, who is responsible for the legality and consequences of these actions? The risk control logic of traditional online payment systems is based on a fundamental premise: a human user actively and in real time performing transactions. Any non-human, automated payment request will be labeled as high-risk by the existing financial system. This is the most fundamental obstacle to the development of the agent economy. Specifically, a transaction request initiated by an AI poses three unanswered questions for merchants and payment networks: Proof of Authorization: How can we confirm that the user has truly authorized the AI to conduct the transaction? Intent Fidelity: Does the AI's specific request (e.g., purchase quantity, price) accurately reflect the user's true intent, especially when the user isn't monitoring the transaction in real time? Liability: If a transaction goes awry, who bears the resulting losses? Google's AP2 protocol was designed to systematically answer these three questions. AP2 aims to establish an open, standardized authorization and trust communication protocol between users, AI agents, and merchants. AP2's core technology is a dual authorization mechanism: 1. Real-time Authorization (Cart Mandate) This mechanism is familiar to credit card users. When you pay with a credit card on a website, an authorization window pops up on your phone. AP2 performs this function. After completing preliminary work such as product research and price comparison, the agent generates a "shopping cart" containing all transaction details and presents it to the user. The transaction must be completed after the user's final review and signature confirmation. Impact Analysis: Real-time authorization is an early form of agent-based payment. It lowers the user barrier to entry but does not fully leverage the agent's autonomy. Its primary value lies in optimizing the pre-transaction decision-making process, but the final "finishing touch" still requires human intervention. 2. Intent Mandate: This is the revolutionary aspect of AP2. Users can pre-set complex, conditional intents. For example, "Only if the price of a Tesla Model Y drops by more than 20,000 yuan, automatically execute the purchase process, using funds from Account A. If Account A is insufficient, use Account B." To make this "forward intent" trustworthy and secure, AP2's implementation logic is based on Verifiable Credentials (VCs): The user's complex intent conditions are compiled into a cryptographically signed, tamper-proof digital "transaction contract" (VC). At every critical step in executing a task, the AI Agent must present this "contract" to prove the legitimacy of its actions. Each execution step also generates corresponding VC records, ensuring the auditability of the entire process. For merchants and clearing networks (such as Visa), they receive a "transaction contract" with the user's cryptographic signature, clearly outlining the scope and conditions of authorization. By verifying this contract, they gain confidence in the legitimacy of the transaction, significantly reducing fraud risk and compliance costs. For users, this makes truly "24/7 autonomous economic agents" possible, freeing them from frequent, repetitive decision-making. In short, AP2's true innovation lies not in transforming clearing networks like Visa or stablecoins, but in adding a trust semantic layer on top of them, ensuring that "who is spending, why they are spending, and whether any amounts outside of authorized scope can be traced." It strives to uniformly resolve the issue of confirming AI payment intent across diverse clearing systems, including fiat and crypto, thereby alleviating the fundamental concerns of all participants regarding the uncontrollable and unverifiable nature of agent behavior. x402 Extension: Natively Embedding Crypto Payments into the Agent's Service Call Process AP2's core technology is designed to make AI agents compatible with existing human-centric financial authorization systems, while the "A2A x402" extension is designed for future native, on-chain A2A payments. The x402 extension is jointly driven by Google, Coinbase, and the Ethereum Foundation. The technical design of x402 stems from a long-dormant internet standard: the HTTP 402 status code, which defines "Payment Required." In the past, due to the lack of standardized machine payment methods, this status code was rarely used in practice. However, as the primary callers of APIs shift from human developers to high-frequency, automated AI agents, the value of this protocol becomes increasingly apparent. Traditional subscription or prepaid payment models are too cumbersome and inefficient for agents to call massive amounts of services on-demand, dynamically, and across platforms. The core concept of x402 addresses this problem by natively coupling API calls with payment. The workflow is designed to be extremely simple and automated: The AI agent calls a paid service API. The service server directly returns a 402 Payment Required response, which contains the information required to complete the payment (such as the recipient address, amount, and token type). The agent's built-in wallet or payment module parses this "payment bill" and automatically completes the payment on-chain (for example, using USDC). The agent then re-initiates the API request with the on-chain payment credentials. After the server verifies the payment, it immediately returns the required data or service results to the agent. This process may seem simple, but it leverages the real-time settlement and highly programmable nature of stablecoins to significantly impact the business model of AI agent services. Impact on AI Agents: True "pay-as-you-go": Agents no longer need to go through the traditional manual process of "registering an account -> linking a credit card -> selecting a subscription plan -> waiting for service activation." They can dynamically discover any X402-compliant service on the network and complete payment and usage in milliseconds, laying the foundation for the agent's autonomy and exploration capabilities. Extreme Cost Efficiency: Agents can make payments far more frequently than humans, and their parallelized task processing capabilities enable extremely granular micropayments and stream payments. x402 enables agents to pay precisely based on the number of API calls, the amount of data returned (tokens), and the duration of computation, seamlessly aligning resource consumption with costs and avoiding the resource waste inherent in traditional subscription models. Impact on AI Service Providers: Downscaling the Payment System to the Protocol Layer: x402 builds "access as quote, payment as service" capabilities directly into the communication protocol layer. Developers no longer need to build complex billing, account, and subscription management systems. They can focus on developing core services and fine-tune pricing and market launch for any API, data shard, or even a page component. Optimizing Cash Flow and Global Settlement: Leveraging stablecoins, service providers can achieve instant, low-cost global settlement, eliminating the lengthy settlement cycles and high fees associated with traditional cross-border payments. This provides unprecedented convenience for reconciling ultra-high-frequency, extremely small-value transactions, significantly lowering the barrier to commercializing AI services for individual developers and small teams. Summary and Outlook: Dual Tracks in Parallel, an Emerging Ecosystem The value of the AP2 protocol lies not in its seemingly rudimentary technical details, but in the fact that it provides the final piece of the commercial closed-loop puzzle—value settlement—in the technological evolution path of MCP (connectivity) + A2A (collaboration). By empowering agents with reliable transaction capabilities, it paves the way for the industry's transition from the "AI tool" era to a true "agent economy," paving the way for a standardized path forward. Google's deployment clearly demonstrates its intentions: on the one hand, by integrating with traditional commerce, it aims to accelerate the adoption of agents in mainstream scenarios like e-commerce and subscriptions, establish industry standards, and enable e-commerce platforms to quickly adapt to AI intervention. On the other hand, by embracing crypto-native solutions, it also establishes standards for on-chain agent-to-agent payments. Opportunities and Challenges: The Crossroads of Crypto-Native Agents The completion of AP2's payment capabilities will undoubtedly unlock a wide range of application scenarios, such as 24/7 self-managed finance and automated enterprise procurement and renewals, all of which will drive the penetration of the agent-to-agent economy. However, AP2 is a top-down authorization framework driven by tech giants, primarily focused on addressing their core business ecosystem issues. Crypto payments play a role of "compatibility" and "support." This creates a positive stimulus for crypto-native agent protocols. Currently, agent products truly embodying the core values of Web3, such as decentralization, censorship resistance, and privacy protection, have yet to achieve key technological breakthroughs, and lack a clear PMF. As industry standards gradually become clearer, the key factor in the future competition among crypto AI protocols will be who can create products with native crypto qualities and innovative designs. Although the future remains uncertain, some protocols in the crypto industry have already begun actively developing around AP2 and the possibilities it brings. From the initial partners, we can see that a preliminary ecosystem is taking shape. Standards and Infrastructure: Coinbase, as the original proponent of the x402 payment standard, is actively promoting its implementation; the Ethereum Foundation is diligently developing underlying protocols such as ERC-8004 (the Trusted Agent Standard). Portals and Wallets: MetaMask is committed to building a self-hosted AI agent wallet and simplifying the user onboarding process, striving to become a secure entry point for the agent economy. Public Chains and Interoperability: Mesh focuses on optimizing payment routing to ensure the success and efficiency of agent payments. Payments and Applications: Companies like Crossmint and BVNK provide agents with multi-channel crypto and fiat payment capabilities; platforms like Questflow already have built-in x402 micropayment systems, allowing agents to be compensated based on task results. Ultimately, the release of AP2 marks the beginning of the standardization process for the Agent Economy. It opens up unprecedented possibilities for the industry, but also further centralizes the power that controls these new economic rules. For the crypto industry, this presents both an excellent opportunity to integrate into the mainstream and accelerate adoption, and a critical test of the future dominance of the Agent Economy. How to leverage this infrastructure while forging a differentiated path embodying the core values of Web3 will be a track worth watching in the coming year.
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