The Agentic Shift: Why Financial Infrastructure Must Evolve for the Autonomous Economy

For decades, the primary objective of the global payments industry has been binary and defensive: distinguish the human from the bot. Every innovation—from 3D Secure protocols and CVV verification to behavioral biometrics—was designed to keep the "bad bot" out of the financial ecosystem. However, a seismic shift is underway. We are entering the era of "Agentic Commerce," where bots are no longer just potential threats; they are becoming the primary customers.

As autonomous AI agents gain the ability to research, negotiate, and execute transactions, the very foundations of our payment infrastructure are being tested. Stockholm-based FinTech firm SolvaPay is at the forefront of this transition, building the bridges necessary to allow AI to operate within the constraints of traditional finance.

The Collision of Autonomy and Compliance

The challenge of agentic commerce is not just technical; it is philosophical. For the better part of a century, financial systems have been built on the assumption that a human sits at the end of every transaction. This assumption is deeply embedded in the regulations, security protocols, and reconciliation processes that govern the movement of money.

Viggo Stenseth, CEO and co-founder of SolvaPay, notes that while AI agents can now perform complex tasks—such as software utilization, information synthesis, and workflow management—the actual act of moving money remains tethered to human-centric assumptions.

"We built really robust systems to verify that there is a human doing the transaction—3DS, CVV codes, everything to prevent a bad bot from doing things," Stenseth said. "It is so deeply ingrained in the infrastructure that transitioning to a world where a bot should be doing these things creates a fundamental friction."

For merchants, the dilemma is immediate. They cannot simply disable fraud detection and security protocols to accommodate authorized agents. They must find a way to verify the legitimacy of an AI buyer, ensure it has the authority to spend, and confirm the source of its funds, all while maintaining the stringent anti-money laundering (AML) and compliance standards required by law.

Chronology: From the Web to the Agentic Revolution

To understand the urgency of this transition, it is helpful to view it through a historical lens. Stenseth draws parallels between the current state of agentic AI and the early days of the internet.

• The 1990s (The Skepticism Era): During the dawn of the internet, many businesses were fundamentally unconvinced that an "online presence" was necessary. It took years for the infrastructure of eCommerce to catch up with the vision of a digital marketplace.
• The 2010s (The Mobile Shift): Similarly, smartphones arrived and gained mass adoption before many companies had built robust, mobile-first payment experiences. Businesses were playing catch-up to consumer behavior.
• The Present (The Accelerated Timeline): Unlike the gradual adoption of the web or mobile, agentic AI has developed on an aggressive, accelerated timetable. "The timer already started a couple of years ago," Stenseth notes.

While the software development side of AI has advanced at breakneck speeds, the financial "plumbing"—the billing, the payment settlement, and the flow of capital—has lagged significantly. We are now in a race to align the speed of autonomous software with the deliberate, highly regulated speed of financial rails.

The Multi-Layered Nature of Agentic Transactions

One of the most significant complexities of agentic commerce is that an agent does not transact like a person. When a human buys a product, it is usually a single, discrete event. When an agent is assigned a complex goal, it often decomposes that goal into a hierarchy of sub-tasks.

A Cascade of Payments

Imagine an agent tasked with researching and compiling a market report. To do this, the primary agent might hire a specialized "data-scraping" agent. That agent, in turn, might pay for API access from a third-party provider, and perhaps another agent to format the data.

This creates a "nested" payment structure where one initial assignment triggers a series of downstream transactions. Stenseth highlights that this creates unprecedented reconciliation challenges: "We have built systems around solving one single transaction. When you have transactions running several layers deep, the question becomes: if something fails at the end of the chain, do we roll the whole thing back, or do we isolate the failure?"

The Buyer-Seller Duality

Agents are also blurring the lines between producers and consumers. In a scenario described by Stenseth, a developer might build an agent that autonomously pulls data, pays for information services, applies proprietary analytical models, and then resells that refined data to other users.

In this ecosystem, the agent is both a buyer (paying for data inputs) and a seller (charging for its output). This dynamic favors usage-based billing—where payments are calculated based on individual API calls or data points—rather than the traditional monthly software subscription models that currently dominate the SaaS landscape.

Implications for the Future of Commerce

The shift toward agentic commerce will force a complete re-engineering of how businesses interact with the financial system. The implications are far-reaching:

1. The Evolution of Fraud Detection

Fraud systems can no longer rely on simple "bot vs. human" heuristics. They must evolve to distinguish between "authorized agents" and "malicious actors." This will require a new layer of identity verification specifically for AI, perhaps utilizing cryptographic keys or verified AI signatures to ensure that the agent acting on a merchant’s site is indeed the entity it claims to be.

2. Programmable Financial Controls

Businesses will need to implement "guardrails" for their agents. This includes defining specific spending limits, approved vendors, and asset types. SolvaPay is working to build these controls directly into the payment infrastructure, allowing companies to define "what" an agent can spend and "whose" money it is using, all while keeping the transaction within the established, regulated financial system.

3. The Agent as a Spending Optimizer

Looking further into the future, the agent may become a personal financial advisor. Stenseth imagines a world where a consumer gives an agent a goal—such as "optimize for airline miles." The agent would then autonomously analyze the merchant, the reward structure of the user’s various credit cards, and the transaction fees to select the payment method that yields the highest return for the consumer. The agent, in this instance, becomes the ultimate arbiter of the "best" payment choice.

Conclusion: Bridging the Gap

SolvaPay’s mission is to act as the connective tissue between the fast-moving world of autonomous software and the slow, secure, and regulatory-heavy world of global finance. As Stenseth emphasizes, "You can’t skip steps."

The industry cannot bypass the requirements of AML, ledger integrity, and licensing. Instead, the goal is to build an abstraction layer that allows AI agents to interact with these systems natively. As agentic commerce moves from a theoretical novelty to a standard operational model, the firms that succeed will be those that view autonomous software not as a threat to be blocked, but as a new class of customer that requires its own unique, robust, and programmable financial infrastructure.

The era of the bot-as-consumer is no longer on the horizon; it is here. The question is no longer whether we can keep the bots out, but how effectively we can welcome them into the economy.