As AI agents gain the ability to independently access external services and purchase computing power, data, and other digital resources, payments become part of programmatic interactions. In these scenarios, financial transactions can be initiated, authorized, and completed without direct human involvement.
What Are Machine and Agentic Payments?
Two sets of terms are used to describe payment transactions that are largely initiated and processed by software systems:
Machine payments (Machine-to-Machine, or M2M payments)
Agentic payments (Agent-to-Agent, or A2A payments)
There’s no established distinction between these terms yet. For clarity, this article treats M2M payments as the broader category of programmatically initiated transactions between digital systems, while A2A payments are a specific case in which the parties involved are AI agents.
It’s worth noting that money doesn’t move directly between these digital actors. They exchange information about the service, price, and payment terms, generate payment instructions, and confirm execution. The actual settlement takes place through traditional payment infrastructure or using alternative payment methods.
What A2A Means in Different Contexts
In the context of agentic finance, A2A payments are a type of automated process that allows agents to handle the entire flow, from identifying a need for a specific commercial service to confirming settlement.
In the financial industry, however, A2A payments traditionally refer to Account-to-Account transactions, or payments made between bank accounts. As agentic finance developed, A2A also came to mean Agent-to-Agent in payment scenarios involving AI agents. This distinction matters because the term A2A payments can refer to different types of financial transactions depending on the context.
The A2A abbreviation can create further ambiguity because of the Agent2Agent (A2A) Protocol, an open standard for communication between AI agents. Google introduced it in April 2025 and transferred the project to the Linux Foundation in June of that year. The protocol allows agents to exchange data, delegate tasks, and receive results, but payments aren’t part of its core functionality.
How Machine Payments Work
The exact sequence depends on the protocol and payment instrument used, but a typical process can be broken down into several stages:
Provider discovery and request creation. The agent identifies an external service capable of performing the task and obtains information about the available service. In a full A2A scenario, the counterparty may also be an agent representing the provider and authorized to negotiate the terms of execution.
Receiving payment terms. The provider returns a machine-readable payment request specifying the amount, currency or asset, recipient, accepted payment method, offer expiration, and other parameters. The agent doesn’t need to navigate a checkout page because the payment terms are delivered directly in machine-readable form.
Permission checks. Before the transaction can proceed, the system must determine whether the payment complies with the rules set by the agent’s owner, including the available budget, maximum transaction amount, permitted service categories, and other restrictions.
Creating payment authorization. If the transaction complies with the established rules, the agent uses an available payment instrument to create a verifiable authorization, or credential, that the receiving party can validate. Its exact form depends on the technology. For example, it may be a cryptographically signed authorization to transfer funds or the data required to process a card payment.
Verification, execution, and settlement. The recipient or payment intermediary verifies the payment authorization, after which the transaction is processed according to the selected settlement model.
Confirming the outcome. After a successful payment, the provider returns the transaction result and payment confirmation to the agent. For a digital service, this can mean receiving both the paid resource and confirmation that settlement was completed. The agent can then continue with its primary task or initiate another transaction.
A machine payment therefore follows a connected cycle: request → terms → permission checks → payment authorization → transaction processing → result. This sequence is a conceptual model, however, rather than a single industry standard.
No Single Standard for M2M Payments
As of October 2026, there’s no single industry standard for machine payments. Different approaches address different aspects of automating financial interactions between digital systems.
These approaches can be broadly divided into several areas:
Embedding payments into programmatic interactions between clients and services. In this model, payment terms and related actions become part of the broader data exchange.
Managing an agent’s payment permissions. These models define which transactions an agent is allowed to perform, within what limits, and under what conditions.
Integrating with existing payment infrastructure. Some solutions connect agentic scenarios to card, banking, or blockchain-based settlement mechanisms.
The relevant specifications and technological approaches continue to evolve, and the boundaries between individual models remain fluid. Some capabilities, including the delegation of payment permissions between agents, are only beginning to be formalized and don’t yet have a standardized implementation.
M2M Settlement Models
One key consideration in M2M payments is when funds should move relative to the delivery of a service. For a simple digital resource, the provider may require payment upfront. In other cases, the agent first provides payment authorization, the service performs the requested work, and the final amount is charged upon completion. Services with variable pricing may use fund reservations or payment sessions.
The x402 protocol, for example, formally defines several models:
Authorization. Funds move after the request is successfully completed.
Upfront. Settlement takes place before the resource is provided.
Escrow. Funds are reserved in advance, with the final amount determined later.
The choice of model depends on how risk is allocated between the parties. The provider wants assurance that it will be paid, while the purchasing agent needs to avoid paying for a service that wasn’t delivered or didn’t meet the agreed terms.
M2M Payments in Practice
Today, M2M payment mechanisms are most clearly implemented through specialized protocols such as x402 and MPP.
x402: An Open, Internet-Native Payment Protocol
In a typical HTTP scenario, x402 embeds payment directly into the data exchange between a client and server:
If a resource requires payment, the server responds with the 402 Payment Required status code and provides the accepted payment parameters.
The agent creates a payment authorization and resubmits the request.
After verification, the server performs the requested operation and returns the result along with settlement information.
However, x402 v2 isn’t limited to HTTP. The protocol specification allows it to work with other forms of data exchange between systems, so the specific interaction flow may vary.
MPP: An Open Machine Payments Protocol
The Machine Payments Protocol (MPP), developed by Stripe and Tempo, is an open protocol for programmatic payments designed to work independently of any specific payment method. It can support stablecoins, card infrastructure, and other payment methods, as well as both individual transactions and longer-running payment interactions.
A typical MPP flow works as follows:
The service responds to an unpaid request with a payment request, or challenge.
The agent takes the required steps and resubmits the request with a payment authorization or credential.
After successful verification, the agent receives the resource and a payment receipt.
MPP also provides a mechanism for continuously consumed services. Within a payment session, the client establishes a payment context, and the cumulative authorized amount can increase as the service is used. Additional funds can be added when needed, while any unused balance can be returned when the session ends. This model can be used for API requests, LLM tokens, or data transfer, for example, and avoids running a full authorization process for every individual unit of consumption.
What Happens When Two Agents Interact
In a pure A2A scenario, both parties can participate in structuring the transaction. Consider a corporate agent that needs specialized data analysis. It finds a provider’s agent, communicates its requirements, and receives an offer specifying the type of output, delivery time, and price. After reviewing the terms, the first agent confirms the order within its allocated budget. The provider sends a payment request, the buyer creates a payment authorization, and, once the payment is verified, the remote agent performs the task and returns the result.
The interaction doesn’t have to be limited to a single payment. One agent can engage several external services in sequence. It can purchase data, pay for computing resources, call a specialized LLM, and pass the final result to another participant. Each step becomes a separate machine transaction within a longer chain of interactions.
How Machine Payments Differ From Traditional Payment Automation
Aspect | Machine / Agentic Payments | Predefined Automated Payments |
Trigger | Can arise dynamically as an agent performs a task | Usually follows a predefined schedule, event, or rule |
Payment parameters | Amount, recipient, or service may be determined during execution | Core parameters are generally established in advance |
Counterparty | May be selected by the agent as part of the task | Usually known when the payment arrangement is configured |
Authorization | Depends on the agent’s permissions, limits, and applicable approval rules | Usually granted when the automated payment is set up |
Role of software | Can discover services, evaluate terms, authorize, and initiate payments | Primarily executes previously defined payment instructions |
Human involvement | Can range from approval of each transaction to predefined autonomous limits | Typically concentrated at the setup or modification stage |
Human Involvement in Authorizing Agent Transactions
The autonomous nature of agentic and M2M payments doesn’t mean users or organizations give up control. The degree of human involvement depends on the authorization model. Broadly, there are two main scenarios:
Approval of individual transactions. An agent can independently find a service, negotiate terms, and create a payment request, but a person must provide additional approval before a purchase or specific transaction can proceed.
Fully autonomous model. A person or organization sets the budget, the agent’s permitted actions, spending categories, recipients, and other restrictions in advance. The agent can then conduct transactions independently within those parameters.
Mastercard’s Agent Pay for Machines (AP4M) service, for example, follows the second model. The agent receives verifiable permissions, while authorization rules and spending limits are enforced programmatically.
Amazon’s AgentCore Payments uses a similar approach. The developer sets payment restrictions at the infrastructure level, while the agent can independently pay for access to APIs, MCP servers, and content through x402 or MPP.
Machine payments therefore move settlement, along with the related terms, permissions, and control mechanisms, into programmatic interactions. This allows AI agents to pay for goods and services with varying degrees of autonomy while operating within rules set by the user or organization.
