Introduction
Agentic AI is one of the hottest topics in the payments space. To cut through the hype, Alla Gancz joined the Voice of MPE podcast to explore how the technology is redefining retail consumer journeys, transforming the checkout and payment experience.
The announcement from OpenAI and Stripe introducing instant checkout and the Agentic Commerce Protocol (ACP) marked a real turning point for retail and payments. For the first time, a conversational AI platform stopped being merely an intermediary for information and became a transactional environment, where search, recommendation and checkout merge into a single seamless experience. Since then, early rollouts, notably ChatGPT's Instant Checkout, launched in early 2026 with Etsy sellers, have confirmed the promise while also exposing operational limits, prompting several players to shift toward journeys orchestrated more from merchant apps than directly inside the chat thread.
What is agentic AI? What sets it apart from today's AI in payments?
Unlike traditional AI tools, which rely on humans to drive every step, providing inputs, interpreting outputs, and deciding what to do next, AI agents operate with autonomy and intent.
Defining agentic AI, Agentic AI refers to goal-driven systems that don't simply react to prompts but can reason, plan and act independently on behalf of humans. In contrast to today's AI tools in payments, such as fraud-detection models flagging anomalies, chatbots handling FAQs, or recommendation engines suggesting offers, agentic AI can execute entire end-to-end processes.
It doesn't just assist with tasks; it delivers outcomes. The difference is fundamental: a chatbot answers your questions about a product. An AI agent finds the product, compares prices, applies your loyalty preferences, and completes the purchase, all without you having to navigate through multiple pages or systems.
Agentic commerce in practice, As a result, agentic commerce will see agents undertaking shopping tasks for customers, including finding products, tailoring recommendations, and completing transactions automatically.
Advances in AI are enabling these digital assistants to meet customer demand for seamless, personalized shopping. Merchant priorities are shifting from making their products appear in search engines to making sure their products are front and center for AI systems.
At the heart of these processes lies the integration of sophisticated payment solutions to facilitate smooth transactions.
The authorization and authentication challenge, This autonomous purchasing capability removes friction for the customer, but places additional burdens on merchants. They must establish a shared foundation to authenticate, validate and securely convey an agent's authority to transact.
While today's payment systems generally assume a human clicks the 'buy' button, autonomous agents will be able to initiate payments. AI agents transacting on behalf of users need a secure foundation to authenticate, validate and convey their transactional authority.
Today's payment systems assume direct human interaction, but autonomous agents initiating payments challenge that assumption, raising critical questions:
Authorization: Proving that a user has given an agent the specific authority to make a particular purchase. How do we know the agent is truly acting according to the user's wishes and hasn't been compromised or misconfigured?
Authenticity: Allowing a merchant to be confident that an agent's request accurately reflects the user's real intent. In a world where agents can be programmed or influenced by third parties, how do we guarantee the transaction truly reflects what the user wanted?
Accountability: Determining liability if a fraudulent or incorrect transaction occurs. If an AI agent makes a bad purchase, who is responsible? The user who set up the agent? The agent's developer? The merchant who accepted the transaction?
Emerging protocols
And we're already seeing attempts to address this. Take Google Cloud's new Agent Payments Protocol (AP2), which provides a payments-agnostic framework so that users, merchants and payment providers can initiate and transact agent-driven payments. Google worked with more than 60 partners to create this protocol.
At Adservio, we're observing the emergence of several competing protocols, and the industry hasn't yet converged on a single standard. This fragmentation creates challenges for merchants and payment providers who must decide where to invest. We recommend a flexible approach that allows interoperability between protocols, at least in the early phases of this evolution.
The landscape has grown more complex since: Coinbase launched x402, a stablecoin settlement protocol taken over in early 2026 by a dedicated foundation hosted under the Linux Foundation, while Stripe and Tempo unveiled the Machine Payments Protocol (MPP), which lets an agent pre-authorize a spending cap and then stream micro-payments continuously, in both stablecoins and fiat currency. In practice, these protocols are proving more complementary than competing: AP2 for end-to-end authorization, ACP for conversational e-commerce checkout, x402 or MPP for machine-to-machine settlement.
How will agentic AI impact consumer journeys?
For consumers, the promise is twofold:
More choice: Access to richer, more personalized and more contextual recommendations. AI agents can consider a far wider range of options than a human could reasonably evaluate, factoring in not just price but also reviews, merchant history, delivery options, environmental impact, and countless other factors.
Less friction: Purchases can be completed in seconds, without navigating through multiple pages. The days of filling out address forms, entering credit card information, and clicking through multiple confirmation steps may soon feel as archaic as using a paper phone directory.
The invisibility of commerce, Customer journeys will become hands-off, hyper-optimized and invisible. Instead of manually managing checkout, renewing subscriptions, or tracking loyalty points, consumers will delegate these to AI agents.
For example:
At checkout, an AI agent could compare multiple merchants in real time, find the best deal, apply loyalty points, and pay seamlessly in the background. Imagine never having to wonder whether you're getting the best price, or whether you forgot to use a promo code. The agent handles all of it automatically.
For subscriptions, agents could renew, cancel, or switch providers automatically based on price or usage changes. Think of Netflix automatically pausing your subscription when you travel, or downgrading your plan during months when you watch less.
Loyalty becomes proactive: Delta Airlines is already experimenting with AI to optimize refunds. In the future, agents could automatically upgrade you to business class using your points, or make sure you never lose out on benefits because of a missed deadline.
The challenge for merchants, This means much less direct interaction between merchants and customers. Instead, merchants will need to make their offerings machine-readable and agent-friendly, because they are now competing for placement in AI agents' decision logic, not just on websites or apps.
At Adservio, we help our retail clients understand that the future of merchandising isn't just visual, it's semantic. Beautiful product photos remain important for humans, but AI agents need rich structured data: precise specifications, clear comparisons, explicit return policies, all in a format machines can interpret and compare.
What practical use cases are already emerging today?
It's still early days, but we're already seeing powerful applications of agentic AI, for example:
AI shopping assistants comparing prices and making purchases: Klarna's AI assistant (powered by OpenAI) can compare prices and even complete purchases. In its first few months, it handled two-thirds of Klarna's customer service chats, demonstrating the scale of the potential.
Automated bill management handling due dates and payments: Apps like Cleo or Mint already schedule payments and optimize balances. Agentic AI will make this fully hands-free, managing not just when to pay but potentially negotiating better terms or switching providers if better deals emerge.
Dynamic fraud prevention spotting unusual patterns in real time: Mastercard is testing AI models that adapt in real time to new fraud patterns, rather than relying on fixed rules. This is crucial, as fraudsters are also becoming more sophisticated, potentially using AI for their attacks.
Personalized shopping agents curating deals and optimizing loyalty rewards: These agents learn your preferences over time, understanding not just what you buy but why, and can make suggestions that balance price, quality, sustainability and other factors that matter to you.
Payment leaders take a stance, Global payment leaders are moving fast to make their networks agent-ready, giving developers and businesses secure, scalable infrastructure for autonomous transactions and commerce:
Visa, with its Intelligent Commerce platform, lets AI agents discover, select and securely purchase products on consumers' behalf.
Mastercard has launched its own Agent Pay platform, creating a standardized way for agents to initiate, authorize and settle payments.
PayPal, with its Agent Toolkit, lets developers build sophisticated agentic workflows that handle payments.
In the near term, as the technology continues to mature, expect merchants to experiment with the services they can offer through agentic channels.
What opportunities do merchants have, and how can they prepare?
We're already seeing agentic AI use cases move from concept to reality, and we see three main channels through which retailers can activate agentic commerce.
Business to Agent (B2A), Participate in agentic channels by making your product catalogs and checkout flows accessible to third-party AI platforms such as Perplexity or ChatGPT.
This channel represents perhaps the biggest opportunity and the biggest risk. Opportunity because it gives access to millions of users who prefer to interact via AI assistants. Risk because you lose direct control over the customer experience and must trust the AI platform to represent your products faithfully.
At Adservio, we recommend merchants treat B2A as a strategic distribution channel, similar to how they approached Amazon a decade ago. You need to be there, but you also need to protect your brand and your margin.
Agent to Consumer (A2C), Build agentic experiences into your own site or app through branded shopping assistants or natural-language interfaces, like Saks Fifth Avenue's AI agent, to improve both customer experience and conversion.
This channel offers more control but requires greater investment. You build your own agentic capability rather than relying on third-party platforms. The advantage is that you retain the customer relationship and capture all interaction data.
For premium brands or retailers with loyal customer bases, A2C could be the priority strategy. You create a differentiated agentic experience that reflects your brand and values.
Agent to Agent (A2A), Power autonomous purchasing agents by enabling software-triggered transactions through subscriptions or replenishment flows, like a personal grocery AI agent automatically restocking across multiple retailers when supplies run low.
This is the most futuristic and least mature channel, but potentially the most transformative. In an A2A world, your personal AI agent negotiates with retailers' AI agents to get the best terms, manage deliveries, resolve issues, all without human intervention.
Future outlook and key considerations for banks and payment service providers (PSPs)
Emerging platforms and ecosystems
Platforms and ecosystems, The rise of agentic commerce could create a new layer of platforms that mediate between consumers, agents and merchants. While today's tech giants have an early advantage, new entrants could emerge around AI models or trust infrastructure.
What makes this shift different is that influence will move from controlling consumer interfaces to controlling how agents access data, make decisions and execute transactions.
At Adservio, we anticipate the emergence of "trust brokers", specialized organizations that don't sell products themselves but verify agent identity, certify their authorizations, and guarantee regulatory compliance. These brokers could become critical players in the ecosystem.
Data and trust infrastructure
Data and trust infrastructure, Behind the rise of agentic commerce lies a growing race to build the trust layer that will let AI agents identify themselves, prove user consent, and transact securely. Standards bodies are beginning to define the foundations for digital identity and authorization.
We're already seeing major payment networks, cloud providers and fintech platforms trying to extend these standards into commercial frameworks.
New revenue models and new performance indicators
New revenue models, Payment players will continue to control the transaction and settlement layer, but agentic commerce opens up an additional opportunity. Providers can move up the value chain by enabling the services agents depend on, verified identity, authorization, data validation and agent-to-agent transactional support.
These capabilities can evolve into new revenue models built around trust, compliance and interoperability (in addition to existing pure transaction volumes).
At Adservio, we help payment providers develop pricing strategies for these new services. The challenge is that there are no market benchmarks yet, so pricing must balance attracting early adopters with long-term viability.
New KPIs, Agentic commerce will change how performance is measured. Instead of focusing on clicks or conversions, merchants will need to optimize for how their products can be discovered, interpreted and transacted by AI agents.
The new KPIs will look very different from what we know today and will emphasize: Data quality, is your product data complete, accurate, structured? Structured catalog accessibility, can AI agents easily query and understand your catalog? API reliability, are your APIs fast, stable, well documented? Trust or verification scores, what is your algorithmic reputation?
User control and safety tools
User control and safety tools, As agents gain autonomy, users will need clear ways to set limits, permissions and preferences. These tools are likely to surface in banking apps, digital wallets and platform settings, letting consumers review, approve or override agent actions.
We recommend financial services providers see this not as a compliance burden but as a differentiation opportunity. The wallet or banking app that offers the best agent controls, the most granular, intuitive and reliable, will win user trust and preference.
What risks and challenges come with agentic AI in payments?
Key risks include:
Fraud and social engineering, Just as merchants deploy agentic AI, fraudsters will too. We could see AI-powered bots launching social engineering attacks at scale. Payment networks need to build equally advanced defensive agents.
The risk isn't just brute-force attacks but sophisticated attacks that exploit agent logic. For example, a malicious agent could present itself as offering a better price to lure a consumer's agent, then deliver counterfeit products or nothing at all.
Liability and chargebacks, If my AI agent buys the wrong subscription or falls for a scam, who is responsible, me, the merchant, or the AI provider? Current chargeback frameworks weren't designed for autonomous actors.
At Adservio, we anticipate that new dispute-resolution mechanisms will emerge, potentially including "agent arbiters", neutral AIs that can review interaction logs, assess intent and determine liability in a more nuanced way than today's chargeback processes.
Transparency and explainability, Agents can appear to be "black boxes." Regulators, merchants and consumers all need audit trails to see why an agent made a given choice.
The regulatory landscape
European regulators are already moving:
The EU AI Act requires explainability and accountability for high-risk AI systems, which likely includes agents performing financial transactions.
PSD3 and its companion Payment Services Regulation (PSR), successors to PSD2, with political agreement reached in late 2025 and formal adoption expected in 2026, for effective application from 2027-2028,broaden the scope for strong authentication, potentially including the authentication of agents acting on behalf of users.
The Digital Services Act (DSA) establishes rules on transparency and consumer protection, applicable to platforms hosting commercial agents.
This could give Europe a trust advantage, but there's a delicate balance. Over-regulation risks slowing innovation compared with the US or Asia, where adoption may move faster. The winners will be the ecosystems that combine speed and trust.
At Adservio, we help our clients navigate this regulatory complexity, designing systems that are flexible and can adapt to different regulatory regimes across geographies.
What comes next?
That's why this is a step change, not a rebranding. AI is moving from classification and prediction to autonomous action, and adoption is happening faster than previous waves. Fraud models and chatbots took years to mature and earn trust. But with agentic AI, the APIs, cloud infrastructure and regulatory frameworks already exist, so we're likely to see rollout in months, not years, especially in payments, where competition is fierce.
The call to action for payment firms, For payment firms, the call to action is clear:
Build the rails and standards for agent-driven commerce: Invest in the infrastructure that will enable agentic transactions at scale. This isn't just a technical question but also one of standards, governance and ecosystem-building.
Embrace or co-create protocols to enable agentic payments: Don't wait for a single standard to emerge. Actively participate in shaping these standards, bringing your expertise in security, compliance and UX.
Partner across ecosystems to shape adoption: Agentic commerce isn't something a single organization can build. It requires collaboration between payment providers, AI platforms, retailers, regulators and consumer groups.
Conclusion: A new era for commerce
The future of payments isn't just a faster checkout. It's a new era where intelligent agents reshape the entire commerce value chain. Consumers will benefit from more convenience and better choices. Merchants will need to adapt to new ways of connecting with customers. Payment providers have the opportunity to create new value-added services.
At Adservio, we help our clients navigate this transformation by starting with a clear assessment of where they sit on the agentic maturity curve, identifying priority use cases that balance value and feasibility, and progressively building the necessary capabilities.
Agentic commerce isn't a question of if, but of when and how. The organizations that start experimenting and learning now will be the ones that shape the future of digital commerce.
Note: The statements and opinions expressed in this article are those of the authors and do not necessarily reflect the positions of Adservio.
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