We are on the verge of a transformation in digital retail so profound that it deserves strategic attention right now: the rise of AI personal shopping agents.
According to research from McKinsey & Company, the “agentic commerce” model—AI agents that make purchases on behalf of consumers—could move between USD 3 trillion and USD 5 trillion in global B2C retail by 2030.
And the industry is already preparing. The Connected Shoppers Report from Salesforce, which surveyed thousands of retailers and consumers across several countries, indicates that 75% of merchants believe AI personal shopping agents will play a decisive role in e-commerce. Shoppers are anticipating a shift as well. 54% of Gen Z respondents say they already use artificial intelligence to discover new products, and 63% are interested in delegating the purchasing process to AI agents in the future.
Yet not everyone is clear on how to adapt to this new landscape. While many digital business leaders are focusing on agent technology, they’re leaving out the most crucial component: their payment infrastructure. Does it have the resilience, security, and flexibility to support an ecosystem of autonomous transactions?
Preparing the technological foundation of payments is a strategic investment that must begin now, and in this article, we’ll explore this idea in more detail.
What Are AI Personal Shopping Agents?
AI personal shopping agents are artificial intelligence systems capable of acting on behalf of the consumer, carrying out every step of the purchasing journey. This includes identifying a need, finding the best available option, comparing prices, negotiating conditions, and executing the transaction autonomously and securely.
These agents operate through delegated decision-making and execution. The consumer sets parameters and preferences—such as favorite brands, spending limits, replenishment frequency, and priority levels—and the system begins to operate within these guidelines. Based on behavioral data, purchase history, and real-time context such as location, inventory, and promotions, the agent decides what to buy and when.
In other words, AI personal shopping agents are artificial intelligence systems authorized to represent the user in digital purchasing environments.
Instead of merely recommending products—a function widely used in e-commerce marketing today—or automatically filling a cart, the agent completes the purchase, triggering payment methods and confirming the transaction without direct human intervention.
This ability to take delegated action marks the essential difference between today’s recommendation models and the next generation of intelligent agents. While traditional algorithms inform, personal shopping agents act, ushering in a new paradigm in which the consumer experience is driven by invisible transactional infrastructures capable of connecting intent, context, and execution in real time.
What Challenges Come With AI-Agent Commerce?
The large-scale automation of consumption is ushering in an era of transactions initiated by intelligent systems. Purchases made by AI agents involve millions of automated micro-decisions, taking place continuously and invisibly.
But does this shift from a human actor to an autonomous digital agent fundamentally change the nature of payment operations? What new standards of authentication, security, scalability, and compliance will this reality demand?
Below, we break down the challenges this model introduces for e-commerce.
Authentication and Fraud Prevention
One of the first challenges lies in authenticating transactions initiated by machines. How can protocols like 3DS 2.0, which are designed to validate the intent of a human user, be applied when the buyer is an algorithmic agent?
The industry will likely move toward delegated authorization models. In this setup, the consumer grants the agent specific permissions—such as spending limits, conditions, and exceptions—and the payment platform validates the agent’s actions using tokenized credentials and AI-powered behavioral profiles.
However, this new model also increases the potential attack surface. Threats such as algorithmic impersonation or unauthorized use of agents become more plausible. That’s why strong dynamic authentication and advanced behavioral detection will be essential to maintaining trust in this ecosystem.
Scalability and System Performance
Today’s e-commerce infrastructure was designed for natural user interactions with predictable volumes, not for a reality of autonomous purchases without human involvement.
AI personal shopping agents will operate through recurring, fragmented, and simultaneous microtransactions, requiring a highly scalable payments backbone with minimal latency. To make this possible, companies will need to move from centralized architectures to more modular, distributed systems, with APIs that can handle asynchronous workflows, real-time messaging, and parallel processing.
The real challenge is managing the massive volume of transactions while still guaranteeing consistency and traceability—without disrupting the invisible, seamless experience that defines AI-powered purchasing.
Compliance and Data Security Requirements
With decisions and transactions executed by systems, questions of responsibility and legal compliance gain an additional layer of complexity. How can businesses ensure adherence to LGPD and PCI DSS standards when sensitive data circulates among multiple digital agents, third-party APIs, and cloud services?
Automation requires algorithmic governance and transparency so users know when and how their agent is acting on their behalf. This means redefining consent policies, tracking decision logs, and implementing advanced layers of encryption and data segregation.
The Path Forward: The Core Pillars of a Future-Ready Payment Infrastructure
The challenges we have discussed thus far are not simple, but they also do not require a complete reinvention of e-commerce. Instead, strengthening the current technological infrastructure can ensure that AI agents operate with security, fluidity, and scale.
Payment architectures that support interoperability, tokenization, and transactional intelligence already exist today—pillars that define what we call a future-ready infrastructure.
Let’s break them down below.
Tokenization for Security and Seamless Recurrence
Tokenization is the mechanism that enables AI agents to operate autonomously. It transforms sensitive payment data, such as card numbers, into secure cryptographic tokens that can be reused for new purchases without exposing the original data.
This allows an agent to execute recurring transactions, renew subscriptions, or perform automatic replenishments using delegated authentication and in full compliance with PCI DSS Level 1 standards.
Instead of requesting permission for every purchase, the consumer authorizes the agent to operate within predefined parameters, and the payment infrastructure must ensure that this cycle is secure, auditable, and reversible.
API-Driven Flexibility and Interoperability
AI personal shopping agents don’t “visit websites”—they connect to APIs.
All interaction between autonomous systems and e-commerce platforms will take place through robust programming interfaces that allow direct communication between the agent and the payment infrastructure.
An API designed for this type of transaction must be flexible, well-documented, and modular. It must support public-key authentication and allow the customization of approval, refund, and reconciliation logic.
Payment Intelligence Built for Diverse Payment Methods
In the future, AI agents will look not only for the best price, but also for the best payment option. They will analyze terms, limits, fees, cashback, and even the environmental impact of each payment method to optimize the final decision.
This makes payment diversity essential—but not just diversity in general. For cross-border businesses operating in Brazil, it means supporting the local payment methods that matter most to Brazilian consumers, including Pix (the most popular payment method) locally-issued credit cards, digital wallets, and boletos (Brazilian payment slips). These methods are deeply embedded in Brazilian commerce and will be fundamental inputs for the decision-making models of AI shopping agents.
A future-ready payment infrastructure must therefore offer contextual intelligence for each transaction, including understanding the nuances of local payment behaviors, so that the agent can automatically select the most efficient method in every scenario.
Taken together, these three pillars—tokenization for security, API-driven interoperability, and intelligent decisioning across diverse local payment methods—form the backbone of an ecosystem prepared for AI personal shopping agents in Brazil’s unique market landscape.
Preparing Your Payments Operation for the Next Decade
The arrival of AI-powered personal shopping agents will bring a structural shift to how digital commerce operates. In this new paradigm, trust and transaction fluidity will depend on the invisible robustness of the payment infrastructure that enables these agents to act on behalf of consumers.
Preparing for this era means investing in interoperable, tokenized, API-first payment architectures—systems built to support autonomous, intelligent transactions rather than constrain them. Companies that modernize now will be positioned to thrive in this new ecosystem, while those tied to legacy models will face significantly higher adaptation costs.
That’s why choosing technology partners whose infrastructure is already built on these principles is critical. PagBrasil was designed from day one with security, flexibility, and scalability at its core.
Talk to our specialists about how to build a resilient payment architecture—one ready for the innovations that will define Brazil’s e-commerce landscape in the decade ahead.
