Agentic Payments Turn Proof Into Competitive Advantage

Governance must shape how agents are designed, tested and monitored from the start, Volante Technologies Chief Product, Engineering and Delivery Officer Deepak Gupta writes in a new PYMNTS eBook, “Building the Agent-Ready Payments Enterprise.”

The inflection in agentic AI won’t be defined by what agents can do, since that capability is arriving regardless. It will be defined by which banks can prove what their agents did. The next phase of agentic AI in payments will be shaped by a practical question: “How much work can agents take on without weakening control?”

That question guided our development of Vol360i, Volante’s agentic AI solution for payments. Banks are ready for artificial intelligence to do more than summarize information or suggest next steps, but they are not asking for unchecked automation or another AI layer that creates more work for operations, risk or compliance. They need agents that can work within the payment flow, act when evidence is strong, escalate when risk is higher and show exactly how each recommendation was made.

For financial institutions, there is little to no room for trial and error. A payment cannot be fixed casually after the fact. It touches liquidity and the customer’s expectation that money will move as intended. The economics now point the same way. As inference costs fall, running an agent gets cheap, but in payments, the dominant cost is the time spent on investigation and clawback, and the liquidity hit. When intelligence becomes inexpensive, the binding constraint shifts from compute to control, which makes a confidence-based operating model a rational choice.

Agents Need to Live in the Workflow

A payment workflow is not the kind of process where banks can automate first and clean up the consequences later. That’s why it is essential to design true value-adding solutions around agents who act where payment decisions are made.

Vol360i is built around four capabilities:

Sense Agents monitor for risks before they affect performance.

Predict Agents determine the best outcome for each payment, supporting faster, more cost-efficient decisions.

Prevent Agents identify potential failures before they occur, helping reduce customer-impacting errors.

Repair Agents resolve payment issues in real time, so operators spend less time on repetitive exception handling.

Together, these capabilities move banks away from static rules and manual queues. Further, they prepare banks for what comes next: a growing share of payments initiated by agents acting on behalf of humans. The financial institution that can govern agent-initiated payments — verifying intent, scoring confidence and escalating anomalies — will strengthen their competitive advantage as the counterparty becomes non-human.

Governance Is Non-Negotiable

The AI race is also a governance test. Speed only matters if banks can explain and control the decisions that agents make.

Governance must shape how agents are designed, tested and monitored from the start. Banks need to understand why an agent made a recommendation and whether a human accepted, changed or rejected it. Each decision should leave a clear record, including the data behind the recommendation and the confidence level assigned to it.

That record only means something on infrastructure the bank governs — deployed in-tenant, with data kept resident and no payment information passing through shared inference. Governed confidence requires governed compute. For DORA-regulated institutions, private AI deployment is what turns the audit trail from a promise into a control.

That record is what makes a confidence-based operating model practical. When the evidence is strong and the risk is low, an agent can be allowed to do more. When the risk is higher or the signal is unclear, the system should bring an operator back into the decision. Those responses should then improve the model over time, so autonomy expands based on performance rather than assumptions.

Autonomy Must Be Earned

Agentic AI will not remove people from payment operations. It will change where their judgment is needed. The real opportunity is to take repetitive repair work off their plates so they can focus on decisions that carry the most risk or customer impact.

That shift will not happen all at once, and that is by design. Every reviewed decision that is accepted, changed or rejected trains models and widens the band of what agents can safely do unsupervised. Banks that deploy fastest without that record get speed once. Banks that instrument the loop get compounding autonomy, where trust earned on low-risk repairs funds expansion into higher-risk ones. The moat is not the agent, but the governed track record behind it.