On April 14, 2026, Eastern Eight Time, American Express announced an accelerated layout in the AI payment field, launching a development toolkit aimed at "Agentic Commerce," attempting to enable AI agents to directly complete payments and broader economic activities on behalf of users. As algorithms start to "swipe cards," place orders, and renew subscriptions, the frequency and complexity of transactions will increase, accompanying new types of transaction risks and fraud pathways that will also be generated synchronously. American Express, on one hand, proposed purchase protection for erroneous transactions made by AI agents registered within its network, while on the other hand, relying on its own closed-loop network and dispute handling system to absorb the newly added risks. This path may not only reshape the responsibility division in the traditional payment industry but also open a new observation window for innovations in crypto assets and settlement layers.

Machines Swiping Cards for You: AI Agents Enter Daily Consumption

In the vision of Agentic Commerce, users no longer select products one by one and manually confirm payments; instead, they pre-assign preferences, budgets, and constraints to AI agents, who then compare prices, select products, and automatically complete orders and payments across different platforms. Unlike traditional e-commerce that places one-time orders, AI agents can continuously monitor prices and inventory, allowing you to spread purchases across multiple merchants and complete transactions in multiple increments, even dynamically adjusting order structures. This means that payment networks will need to handle higher frequency, more fragmented, and automated transaction flows covering multiple time zones.

The advantages of AI agents in high-frequency, low-value, cross-timezone decision scenarios are very evident: they can monitor discounts, inventory, and exchange rate changes 24/7, completing order and payment decisions in milliseconds without waiting for users to wake up or log back in. Traditional risk control models based on "cardholder behavior patterns" are difficult to directly migrate to this algorithm-driven environment, where behavior patterns change rapidly with model upgrades: previous "normal behavior profiles" established based on time periods, geographic locations, and consumer categories often lose recognition and interpretability when facing an AI agent that can make decisions for thousands of users simultaneously.

When transaction decisions shift from humans to algorithms, the responsible parties and trust boundaries must also be redrawn: is it too broad a user authorization for AI agents, or are there flaws in the developers' model design, or is it the payment network itself that did not respond timely in terms of risk control? In traditional systems, "I did not swipe this card" often means fraud or account leakage, while in an AI agent dominated era, "I did not actively approve this payment" may simply indicate that the user forgot the authorization scope they initially granted to the agent. Defining clear boundaries of responsibility among authorization contracts, algorithmic behavior, and payment network rules will become the most challenging institutional issue in this round of payment infrastructure upgrades.

Who Bears Responsibility for Erroneous Transactions: Amex Provides Protective Clauses for AI Risks

Concerning the issue of responsibility, the first measure offered by American Express is the commitment to provide purchase protection for erroneous transactions made by registered AI agents within its network. Known as Amex Agent Purchase Protection, this arrangement currently has limited public information available, with more details still in the validation stage, but the core direction is very clear: as long as AI agents "misoperate" within the American Express network, cardholders can seek compensation or fund reclamation through existing dispute handling channels, thereby avoiding bearing the costs of machine errors alone.

This protective clause did not arise out of thin air, but rather is embedded within American Express's relatively mature dispute resolution system and closed-loop network architecture. Compared to multi-party open networks, Amex simultaneously possesses a complete data closed loop involving cardholders, merchants, and acquiring institutions, already having the capability to quickly freeze, retrieve, and arbitrate suspicious transactions. By marking AI agents as "special participants" within the network, the newly introduced purchase protection is actually an additional layer added atop existing risk control and compensation mechanisms, absorbing the uncertainties brought by AI into its familiar risk control engine.

For developers, this purchase protection clearly lowers the threshold for access and error costs: as long as AI agents are formally registered under the Amex network, erroneous transactions caused by model flaws or insufficient scene understanding have the opportunity to be buffered by the protection mechanism without relying entirely on the development team to compensate out of pocket. For merchants, clear protective clauses mean that when they accept orders initiated by AI agents, they expect to face a counterpart with clear rules on disputes and refunds, rather than a "black box algorithm" with ambiguous responsibilities and potential frequent chargebacks.

Once such purchase protection arrangements are regarded by users and regulators as the "standard configuration" for AI agent payments, pressure will transmit along the industry chain to other card organizations and payment institutions: either follow suit with similar clauses and allocate additional risk reserves, or raise risk control thresholds when facing AI agent orders, accepting a certain degree of business loss. By pioneering this initiative, American Express is essentially being the first to price AI agent risks, drawing a baseline for risk assumption in the market with its balance sheet and network governance capabilities.

From Tokens to Identity: Legacy Networks Reshape Trust Stacks

American Express is not starting from scratch when it comes to handling AI agent risks. Over the past few years, it has accumulated a complete set of technical and operational capabilities in areas such as tokenized payment certificates, strong identity verification, and multi-factor authentication: static card numbers are replaced with dynamic tokens, sensitive information is layered and isolated between terminals and networks, and cardholder identity is confirmed through device fingerprints, behavioral characteristics, and biometric verification. This entire "tokenization + identity verification" stack can be directly reused in AI agent scenarios to allocate independent credential spaces, permission boundaries, and behavioral trajectories for agents.

The closed-loop network structure enables Amex to form shorter and denser data loops among cardholders, merchants, and institutions, which is particularly prominent when dealing with AI agents. Each transaction initiated by an agent simultaneously maps to user account behavior, merchant acquiring records, and internal network clearing data, allowing abnormal patterns to be identified and intervened in fewer hops and shorter time frames. When an agent initiates an unusually high frequency of low-value payments across multiple countries and categories in a very short time, the closed-loop network can quickly correlate signals from multiple endpoints without waiting for information synchronization between multiple institutions, thereby increasing the identification rate of "out-of-control agents."

In terms of leveraging assets related to Membership, American Express is also trying to find potential directions that combine with the AI experience, but this still falls within the verification category. The larger backdrop is that compared to open network architectures, after large-scale access of AI agents, the differences in risk control response speeds and dispute resolution efficiencies in closed-loop networks will be further amplified: open networks rely on multi-party coordination to complete arbitration and fund rollback, which involves longer links and more participants; while closed-loop networks can accomplish more automated processing within internal rule frameworks, with opportunities for freezing, re-checking, and compensation to all be enclosed within a single system. This is also why, at a stage where AI agent risks are not entirely controllable, participants with stronger internal governance capabilities gain the opportunity to be the first to present visible protection commitments.

Under Currents in the Settlement Layer: Stablecoins as Behind-the-Scenes Tools

In the frontend payment experience, American Express has not hurried to replace the existing card and online payment processes with on-chain assets but has instead focused its exploration of assets like USDC on the potential applications in the inter-institutional settlement layer. According to currently limited disclosed information, specific technical solutions and timelines have not been published, but the direction is relatively clear: the frontend will still maintain the familiar card and account interface for users, while the backend clearing and settlement logic will gradually introduce on-chain tools to improve capital turnover efficiency.

If assets like USDC are used for inter-institutional settlement, the most direct benefits would be the improvement of fund efficiency and cross-border settlement times. Under traditional architectures, cross-border funds often need to go through multiple tiers of agent banks and regional clearinghouses, resulting in long fund transit times and high costs. When some clearing is completed on-chain, it theoretically shortens clearing cycles and reduces reliance on traditional intermediary institutions while maintaining existing compliance frameworks, bringing multinational merchants and cardholders closer to real-time fund availability experiences.

This approach of "unchanged frontend and backend on-chain" is equally sensitive to regulatory perception and compliance costs: what users see remains the familiar American Express brand and local currency-denominated interfaces, KYC and anti-money laundering processes still operate within traditional tracks, while the settlement layer's embrace of assets like USDC is compressed to the level of inter-institutional batch clearing and liquidity management, striving to avoid introducing unnecessary complexity and regulatory uncertainty at the user end. This design aims to allow traditional institutions to carry out the first systematic trial of on-chain financial infrastructure within a relatively controllable range of risks.

As background, the total market value of tokenized commodities is currently around $7 billion, having grown nearly 600% since the beginning of 2025, indicating a rapid expansion trend for on-chain physical and financial assets. In such an environment, if American Express pre-establishes interfaces with assets like USDC at the settlement layer, it has the opportunity to smoothly access a wider array of on-chain settlement assets and scenarios in the future—from cross-border merchant settlements to collaborations with tokenized asset platforms, ensuring its network retains sufficient plasticity in the next stage of the on-chain clearing ecosystem.

Macroeconomic and Crypto Resonance: Payment Giants Betting in the Window Period

The macro environment provides a time coordinate for this round of payment and settlement innovations. The U.S. March PPI year-on-year rate is 4.0%, lower than the market expectation of 4.6%, with marginal easing of inflation pressures, offering a certain correction space for monetary policy and financial market expectations. The uncertainties surrounding inflation paths and interest rate cycles have led to a simultaneous increase in market demand for fund efficiency, cross-border flows, and risk hedging tools, creating real momentum for the upgrade of payment and settlement infrastructures.

On the crypto asset layer, analyst Adam pointed out that while Bitcoin prices have been rebounding for a period, implied volatility for main term options has shown a decline, and Skew has exhibited a clear positive bias. This structure of "rising prices, cooling IV, and enhanced bullish preference," combined with improvements in on-chain liquidity, constitutes a picture of risk assets and infrastructure upgrades resonating in sync: the market is simultaneously re-evaluating growth assets and also seeking more efficient and programmable settlement channels.

Under such a backdrop, it is not coincidental that traditional payment giants like American Express choose to bet on AI agents and USDC settlement at this time. AI agents have the opportunity to capture the next wave of user interaction and business flow entry at the frontend, while on-chain settlement tools represented by USDC may reconstruct the "pipeline" of global fund flows in the background. For institutions deeply rooted in traditional systems, this is both an extension of their own moats and a proactive defense against potential challengers: if they do not actively set rules during this technology and asset migration cycle, they may be forced to adapt to new settlement orders defined by others in the next phase.

Ultimately, this is a game about discourse power. Traditional financial institutions hope to incorporate the next-generation payment and settlement standards into their familiar compliance and governance frameworks through pre-positioning in AI and on-chain settlement, rather than being "surrounded" by decentralized protocols or emerging technology platforms. The choices made by American Express around AI agent purchase protection and USDC applications at the settlement layer are a concentrated manifestation of this game at the payment network level.

Who Controls the Entry to Next-Generation Payment Order

Overall, American Express is using a combination of "purchase protection + closed-loop network" to re-price and attempt to cover risks associated with AI agent-dominated transaction flows: the former uses clear compensation commitments to buffer the tail risks of machine errors, while the latter enhances the recognition and handling efficiency of abnormal agent behavior through shorter data loops and stronger internal governance. This approach is expected to provide a perceivable safety boundary for users, developers, and merchants while AI agents are still in the early experimental phase.

At the settlement layer, American Express has chosen to use assets like USDC as tools, gradually introducing on-chain clearing capabilities while maintaining an unchanged user frontend payment experience, which could become a template for traditional institutions to trial on-chain finance at lower risks: the surface interface remains stable, while deep infrastructure quietly replaces itself, and regulation adjusts along existing pathways rather than starting from scratch. With the expansion of tokenized assets and on-chain liquidity, this "backend-first on-chain" approach has the potential to reserve interfaces for broader on-chain settlement scenarios.

In the coming years, other card organizations and internet giants will almost certainly engage in a new round of competition in directions such as AI agents and on-chain settlement: who will provide standardized responsibilities and risk absorption for AI agents? Who will lead asset selection and technical routes for cross-border on-chain settlements? How will regulators delineate boundaries for innovation and systemic risk red lines? These will all become issues that the payment and crypto industries must collectively address.

As more and more transactions are initiated by AI and settled on-chain, the sovereignty of human users in payment decisions and asset migrations will inevitably face redefinition: are you really "swiping a card," or are you extending the life of an authorized agent? As the scope of authorization widens and backend settlement becomes more abstract, how can we ensure that we still understand and have the ability to withdraw trust from machines and networks at any time? This open question might be the core that is more worth exploring together by the crypto world and traditional finance behind the recent actions of American Express.

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