AI agents are moving from recommending financial actions to actually executing them. They can reconcile invoices, trigger procurement workflows, interact with APIs and increasingly initiate payments under predefined conditions. This means the instruction to move money may no longer come from a person sitting in front of a screen, but from software acting under delegated authority. For the payments industry, this is not only an efficiency gain; it changes how we need to think about accountability.
Historically, compliance has focused heavily on identity: who is the customer, who is the business, and who ultimately owns or controls it. Those questions remain essential, but in the world of agentic payments, identity alone is no longer enough. An agent can be perfectly identified and still initiate the wrong transaction, in the wrong context, at the wrong time. That is why I believe the industry now needs to move beyond simply asking Know Your Agent and start asking a more important question: Can you govern your agent?
Knowing who an agent belongs to solves an identity problem. What determines risk is authority: what the agent is allowed to do, under what conditions, for how long, when that authority must stop, and who remains accountable for its actions. The next evolution of KYA should therefore not be a machine version of KYC, but a control framework built around delegated authority, continuously binding business intent, agent authority and financial execution.
Bind the Agent to an Accountable Principal
The first step is still identity, but the goal is not to create an independent financial identity for AI. Quite the opposite. Any agent capable of initiating financial actions should be bound to a real business principal that has already completed the appropriate KYB, risk assessment and due diligence. The infrastructure should be able to establish which business account the agent represents, who created and authorized it, which credentials it is using, and whether that authorization remains valid.
This relationship must be both technically verifiable and auditable. Financial institutions do not need to determine whether an AI is inherently trustworthy. What they need is a clear chain of accountability: Verified Business → Authorized Agent → Permitted Action → Transaction. Only when each link can be traced back to a real business principal and a valid authorization can machine behavior be governed within a regulated financial environment.
Define the Mandate, Not Just the Limit
Real risk control begins with authority. Traditional payment systems rely heavily on per-transaction limits, daily limits, account limits and API permissions, but those static controls are not sufficient for agentic payments. An agent should not simply receive a spending ceiling. It should operate within a clearly defined business mandate.
For example, a procurement agent may be authorized to pay suppliers, but only to verified counterparties, against valid purchase orders or invoices, from designated accounts, using approved settlement assets and payment routes, and within defined amount and time thresholds. A treasury agent may rebalance liquidity across predefined accounts but should not automatically create new beneficiaries. A payroll agent may pay registered employee accounts but should not be able to redirect funds to unrelated third-party addresses.
The key question is therefore not only, “How much can this agent move?” but also, “Under what business context is this agent allowed to act?” Static limits restrict amounts. Context-bound authorization restricts behavior. For agentic payments, the second is the more important control layer.
Every Transaction Must Prove Why It Was Allowed
The third control layer is provability. Traditional payment logs can usually tell us who paid, who received the funds, how much moved, when the transaction occurred and what its final status was. In an environment where machines act autonomously, that is no longer sufficient. Future audits also need to reconstruct why the agent initiated the transaction at that moment, which business event triggered it, what mandate was active, which conditions were validated, which risk controls were applied, and why the transaction was ultimately approved or rejected.
Agentic payments therefore need both a Transaction Trail and a Decision Trail. Compliance must move from simply recording what happened to proving why the action was allowed to happen. For autonomous finance, a transaction record without a decision record is incomplete.
Risk Controls Must Move Upstream
Agentic payments will also move part of the compliance control environment further upstream. Traditional transaction controls tend to focus on what happens once money is already about to move, but agent-related risk signals may appear much earlier: unusual API invocation frequency, access to previously unused payment capabilities, behaviour outside the expected mandate, attempts to add new beneficiaries, or signs that credentials may have been compromised.
This means at least two control layers need to operate simultaneously.
The first is Agent Behavior Risk, monitoring identity, credentials, authority boundaries, invocation patterns and behavioral anomalies. The second is Financial Transaction Risk, continuing to apply established AML, sanctions screening, transaction monitoring, counterparty controls and other jurisdiction-specific requirements.
The first asks, “Should this agent be allowed to act?” The second asks, “Should this transaction be allowed to settle?” A safe agentic payment requires both answers to be yes.
These controls must work together across the full financial workflow. An agent may be authorized to initiate a payment, for example, but that does not automatically make the beneficiary, funding source, settlement asset or transaction itself acceptable. Authorization to act and approval for a specific transaction are separate decisions, and both must remain enforceable.
Faster Rails Make Authority More Important
This control framework becomes even more important when agentic payments interact with stablecoin infrastructure. Stablecoins can allow value to move more continuously across markets and beyond some traditional banking-hour constraints. For AI agents, that means they may not only make decisions automatically, but execute them faster.
Speed, however, cuts both ways. Faster settlement rails improve commercial efficiency, but they can also magnify the consequences of poorly designed authority. In a stablecoin-enabled environment, the critical issue is therefore not only transaction speed but authorization quality. Whether value moves through banks, local clearing systems, card networks or stablecoins, the business principal, authorization boundary, commercial purpose and decision trail should remain identifiable, verifiable and auditable. Technology can change how money moves, but it should not change who is accountable for it.
The Industry Needs Standards for Agent Authority
I believe the next standardization challenge for the industry is not simply agent identity, but agent authority. If financial institutions can identify “Agent A” but cannot understand whom Agent A represents, why it is permitted to perform an action, and when that permission expires, interoperability remains incomplete.
A truly interoperable framework for agentic payments should therefore be able to communicate Agent Identity, Principal Identity, Business Intent, Authority Scope, Permitted Actions, Transaction Context, Authorization Expiry, Risk Signals, Decision Record and Revocation Status. Only then will institutions exchange more than “which agent is this.” They will understand which agent is acting, whom it represents, why it is authorized to act, and whether that authority remains valid.
Technology will almost always move faster than regulation, but we already have enough mature principles to begin building responsibly. Identity must be verifiable, authority must be bounded, permissions must be revocable, behaviour must be continuously monitored, transactions must be explainable, and accountability must always lead back to a real business principal. The next step is to move these principles out of policy documents and into APIs, permission systems, risk engines and transaction logs.
So I do not believe the role of compliance is to decide whether AI should be allowed to move money. The real task is to define the boundaries within which AI should be allowed to move money.
Know Your Agent is the starting point.
Govern Your Agent is what makes scale possible.
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