There is an emerging idea behind agentic payments.
Digital payments have spent the last decade becoming faster, simpler and more accessible. The next transformation may be more fundamental: payments that can be initiated and executed by software rather than directly by a human.
Unlike traditional AI applications that analyse information or generate recommendations, AI agents are increasingly being designed to perform tasks on a user’s behalf. In financial services, that could eventually mean searching for a product, selecting an approved option, initiating a transaction and completing the payment within predefined rules.
The shift is important because money is different from information. An AI-generated summary can be corrected. An incorrectly executed payment can create an immediate financial loss.
That makes agentic payments as much a question of financial control as technological capability.
What Are Agentic Payments?
Agentic payments refer to financial transactions initiated or executed by AI agents on behalf of a user or organisation, generally within permissions, spending limits and other predefined conditions.
The distinction between conventional AI and agentic AI is important.
A conventional AI system might identify that an electricity bill is due. An agentic system could potentially go further by checking the bill, following an authorised payment rule and executing the transaction.
core concept
User Mandates: Humans or organizations set boundaries, including approved merchants, maximum spending limits, and timeframes.
Tokenization: Systems issue scoped tokens (such as shared payment tokens) rather than exposing raw credit card numbers or bank credentials.
Protocols and Standards: Frameworks like Google’s AP2, OpenAI’s ACP, and card network initiatives (Visa TAP, Mastercard Agent Pay) handle secure authentication and cryptographic intent.
India Is Emerging as a Major Test Market for AI Payments
India’s UPI ecosystem provides a particularly important foundation for the development of AI payments.
According to Reuters, India is preparing a framework that could allow AI agents to conduct certain small digital payments without requiring approval for every individual transaction. The initial focus is expected to be on frequent, low-value purchases, with controls such as spending limits, identity checks, rules and liability provisions forming part of the proposed architecture.
The scale of UPI makes this development significant.
UPI processed 24.51 billion transactions worth approximately ₹29.82 trillion in August 2026, according to the Reuters report.
The infrastructure required for delegated payments is also not entirely new. NPCI’s UPI Circle framework already enables users to delegate payments to secondary users. An October 2025 NPCI addendum extended the framework to certain IoT devices and software profiles, including limited AI-profile use cases, subject to defined limits and security requirements.
The evolution therefore looks less like a sudden replacement of conventional payments and more like the gradual expansion of delegated financial authority.
From Digital Payments to Agentic Commerce
The larger opportunity extends beyond simply automating a UPI transaction.
Agentic commerce could connect discovery, decision-making and payment into one automated process.
Consider a corporate procurement workflow.
An organisation may already have approved suppliers, spending limits and procurement policies. An AI agent could potentially identify a recurring requirement, compare approved suppliers, check the permitted budget and initiate a transaction without requiring an employee to manually complete every repetitive step.
The same principle could apply to consumer payments.
Routine subscriptions, recurring purchases, travel bookings, household expenses and other predictable transactions could eventually become candidates for controlled automation.
The important word is controlled.
Agentic payments do not necessarily mean giving an AI unrestricted access to a bank account.
The more realistic model is delegated authority: the user or organisation establishes the rules, and the AI operates within them.
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The CFO Perspective: Automation Needs a Control Layer
For finance leaders, the attraction of AI in financial services is obvious.
Finance teams process large volumes of repetitive information and transactions. Invoice matching, reconciliation, payment scheduling, expense monitoring, collections and cash-management activities can consume significant amounts of employee time.
AI could potentially automate portions of these workflows.
But financial automation has a different risk profile from ordinary business automation.
A finance function operates through approval matrices, segregation of duties, authorised vendors, transaction limits, reconciliation procedures and audit trails.
AI payment agents will need comparable controls.
The question for a CFO is therefore not simply whether an AI agent can execute a payment.
The more important questions are:
- What is the maximum amount it can spend?
- Which merchants or suppliers can it pay?
- What conditions must be satisfied before payment?
- Which transactions require human approval?
- How can permissions be revoked?
- Can every transaction be audited?
- Who is accountable when an automated decision is wrong?
As AI gains more autonomy, the control architecture surrounding it becomes more important, not less.
The Biggest Risk in Agentic Payments Is Not Speed
Traditional payment fraud already involves stolen credentials, social engineering, compromised devices and manipulated payment instructions.
Agentic payments introduce another potential attack surface: the decision-making process of the AI agent itself.
An agent could potentially act on incorrect information, misunderstand an instruction, select the wrong merchant or be manipulated by malicious inputs.
The financial impact could occur before a human notices the problem.
This makes authentication alone insufficient.
A robust AI payment system will also need permission management, transaction monitoring, spending limits, identity controls, auditability and mechanisms for stopping or reversing inappropriate activity where possible.
NPCI’s existing UPI infrastructure already places importance on transaction controls, reconciliation and dispute mechanisms. Its UPI Circle extension for IoT and software profiles, for example, includes defined transaction and monthly limits.
The emerging agentic model will require these controls to evolve alongside the technology.
Agentic Payments Could Change the Role of Finance Teams
The long-term impact of AI agents in finance may not be the disappearance of finance professionals.
It may be a change in where human attention is used.
Routine transactions are relatively easy to standardise. Exceptions are not.
A finance professional may spend less time checking thousands of routine transactions and more time investigating unusual payments, managing liquidity, reviewing risk and designing financial controls.
This could make the finance function more strategic.
However, automation only creates economic value when the savings exceed the technology, implementation and risk-management costs.
A system that saves employee hours but creates frequent payment errors is not necessarily efficient.
The relevant calculation is closer to:
Value created = productivity gains + financial benefits − technology costs − control costs − risk exposure
That is the metric that should matter to CFOs.
The Future of Digital Payments May Be Selectively Autonomous
The most credible future for autonomous payments is unlikely to be completely autonomous finance.
Instead, financial systems may become selectively autonomous.
Low-value and predictable transactions could be automated.
Transactions outside predefined conditions could require human approval.
Large payments could trigger additional authentication.
Unusual behaviour could automatically pause a transaction for review.
In this model, humans establish the financial policy while AI executes routine decisions within that policy.
That distinction is crucial.
AI does not need unlimited authority to become useful. It needs clearly defined authority.
What Agentic Payments Mean for Fintech
For the fintech industry, agentic payments could create an entirely new layer of competition.
Payment companies may compete not only on transaction speed and cost, but also on how safely they allow AI agents to transact.
Banks may need to rethink authentication and account permissions.
Merchants may need to build systems capable of interacting with purchasing agents rather than only individual consumers.
Enterprise software could increasingly connect procurement, accounting, treasury and payments through AI-driven workflows.
And regulators will have to determine how responsibility should be divided when a transaction is initiated by software acting under delegated authority.
This is why agentic payments are more than another feature added to digital wallets.
They could represent a structural change in the relationship between people, software and money.
What CFOs Should Watch
Businesses do not necessarily need to deploy fully autonomous payment systems immediately.
But finance leaders should begin assessing where controlled AI delegation could make economic sense.
The starting point should be repetitive, measurable and relatively low-risk processes.
A sensible framework would consider:
1. Financial value
Does automation produce a measurable improvement in cost, speed or working capital?
2. Permission design
What exactly is the AI allowed to do?
3. Transaction limits
What monetary thresholds should apply?
4. Human oversight
Which transactions must remain subject to human approval?
5. Auditability
Can the organisation reconstruct why a transaction happened?
6. Accountability
Who is responsible when the system makes an incorrect decision?
7. Risk management
What happens if the agent, its data or its underlying payment infrastructure is compromised?
These questions will become increasingly important as AI in fintech moves from experimentation toward real financial activity.
The Real Product Is Trust
The first phase of digital payments was about convenience.
The next phase was about speed.
The emerging phase of agentic payments may be about delegated trust.
An AI agent that can complete a purchase is more than an assistant. With clear financial and operational limits, agentic payments could fundamentally change how consumers and businesses interact with money.
The real challenge isn’t whether AI can move money. It’s whether we can trust it to do so safely.
India’s planned expansion of UPI into agentic payments makes the development particularly important to watch. The proposed framework is still evolving, and its eventual design, limits and liability structure will determine how quickly the technology can move from experimentation to mainstream use.
Disclaimer
This article is intended for general educational and informational purposes only. It does not constitute financial, investment, legal or professional advice. Agentic payment technologies, regulatory frameworks and commercial availability are evolving, and features, limits and protections may vary by market and provider.
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