Rewiring the Internet: Commerce in the Age of AI Agents

How commerce, payments, and marketing should evolve for an agent-mediated internet

Welcome to a new post in the AI Agents Series - helping AI developers and researchers deploy and make sense of the next step in AI. Some of my previous posts listed the open-source toolkit for AI Agents builders, the economies of scale for foundational AI models, and the infrastructure required to power the agentic AI era.

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A NotebookLM-powered podcast episode discussing this post:

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December 2028. Maria's AI agent is negotiating simultaneously with twelve different vendors for her daughter's upcoming birthday party. Within minutes, it secured the perfect cake from a local bakery (after verifying their nut-free certification), booked an entertainer with stellar safety ratings, and coordinated custom goodie bags filled with each child's favorite treats (after checking allergies and dietary restrictions with the other parents' agents)—all while staying 15% under budget. What would have taken Maria hours of calls, emails, and anxiety about vendor reliability now happens seamlessly through a web of agent-to-agent interactions powered by the new infrastructure we explored in our previous post.

The revolution in web infrastructure we discussed in previous posts isn't just theoretical—it's enabling fundamental changes in how commerce, marketing, and customer service function. As agent passports and trust protocols become standardized, we're witnessing the emergence of entirely new commercial paradigms.

With the recent release of Tasks by OpenAI, which equips ChatGPT—its consumer-facing AI—with the ability to perform tasks behind the scenes on behalf of users, it's now easier than ever to envision a future where ChatGPT seamlessly navigates the web and handles complex operations for us.

ChatGPT can now set reminders and perform recurring actions

Today, we'll explore how an agent-first internet will reshape domains like payments, marketing, support, and localization.

Agentic payments

Remember when online shopping first emerged, and entering your credit card details on a website felt risky? Card networks like Visa and Mastercard and banks like Chase and Barclays had to rapidly adapt to the digital realm, introducing new protocols like CVV codes and secure payment gateways to protect consumers and merchants alike. This evolution was pivotal in building trust and facilitating the e-commerce boom of the early 2000s, giving birth to digital payment giants like Stripe and PayPal.

Similarly, the payment infrastructure that powers today's internet was built around a simple dichotomy: card-present versus card-not-present transactions and the assumption that all payments are human-initiated. The internet already faced a similar shift with the introduction of subscription payments in the early days of e-commerce, in which customers input their payment credentials once, allowing a merchant to charge them repeatedly in the future. Recurring payments, now a dominant commerce mechanism projected to surpass $2.4T in spending by 2028, underscore how critical adaptive payment systems are to supporting global e-commerce. Yet, even more than a decade later, there are still countries like India that impose strict controls on recurring payments, highlighting the complexities of adapting systems to new paradigms.

But what happens when the legitimate transacting party isn’t human at all?

Digital payment systems are built around human users, employing measures like CVV codes and billing address verification to prevent fraud. These methods assume a human is initiating the transaction, making them ill-suited for autonomous agent transactions.

One major challenge is fraud detection and resolution—an area I intimately understand from my time as a PM at Stripe, where I worked closely with card issuers to develop sophisticated fraud prevention systems. The current paradigm relies heavily on human behavioral patterns: typing speed, mouse movements, time spent reviewing checkout pages, and other signals that indicate legitimate human activity. But in an agent-driven world, these signals become obsolete.

During my time at Stripe, we saw how crucial these behavioral signals were for differentiating legitimate transactions from fraudulent ones. The shift to agent-driven commerce demands an entirely new approach. Rather than looking for signs of human behavior, we'll need systems that verify agent legitimacy, authorization scope, and decision-making logic.

This shift raises critical questions for fraud prevention across banks (Chase, Barclays), card networks (Visa, Mastercard), and payment processors (Stripe, PayPal). For instance, how do you establish a chain of trust when an agent purchases on behalf of a user? How do you verify that an agent hasn't been compromised or hijacked? These challenges require fundamentally rethinking our approach to transaction security and fraud prevention in an agent-driven ecosystem.

Fraud resolution in the era of AI agents

Future payment systems could introduce 'agent wallets' with granular spending controls, such as $100 limits for trusted merchants like Amazon and stricter caps for lesser-known websites. These wallets would integrate real-time fraud detection, submitting cryptographic evidence for disputes and maintaining transparent, auditable records of agent actions tied to human authorization.

Visa could introduce an agent-specific flag to the existing payment protocols, indicating this payment was initiated autonomously, along with a trial of reasoning and actions leading to this payment. Meanwhile, Stripe might expand its SDKs to enforce programmable payment rules, ensuring alignment with user-delegated instructions (Stripe has already made its foray into Agentic payments with its recent SDK release).

Stripe’s new Agents SDK supports Vercel’s AI SDK, LangChain, and CrewAI

Beyond preventing fraud, agentic payments face fundamental economic and infrastructural challenges. The existing payment infrastructure wasn't architected for the high-frequency, low-latency transactions that characterize agent interactions. Consider the standard pricing model of payment processors like Stripe: a 2.9% fee plus 30¢ per transaction. While manageable for traditional e-commerce, this fee structure becomes prohibitively expensive when scaled to the myriad micro-transactions that agents might need to execute.

This pricing isn't arbitrary—it reflects the complex web of stakeholders in the traditional payment chain. Card networks like Visa and issuers like Chase have built their business models around these transaction fees. Interestingly, Stripe's recent acquisition of Bridge, a stablecoin payment infrastructure provider, hints at a potential solution. By leveraging blockchain-based payment rails, companies could facilitate agent-to-agent transactions without incurring the expensive overhead of traditional payment networks. This move suggests a growing recognition that the future of payments may require completely new infrastructure, optimized for the unique demands of autonomous agents.

Consider how a new payment protocol might work in practice. An "Agent Payment Protocol" (APP) could include:

• Delegation Chain Verification - a cryptographic proof chain showing the agent's authorization to make specific types of purchases

• Transaction Context Object - machine-readable metadata including:

  • Reasoning trail that led to the purchase decision

  • Reference to specific user preferences/rules that were satisfied

  • Confidence score for the decision

• Smart spending controls with programmable constraints like:

  • Category-specific limits (e.g., $200 for groceries, $50 for entertainment)

  • Merchant-specific trust scores

  • Required human confirmation above certain thresholds

  

Major payment providers could implement this through extensions to existing standards. For instance, Visa's existing 3D Secure protocol could add an agent verification layer, while Stripe's API could introduce new parameters for agent-specific transaction metadata.

While payment infrastructure provides the foundation for agent-driven commerce, the very nature of how we complete transactions must also evolve. The familiar checkout process—a hallmark of e-commerce for decades—is about to undergo its own transformation.

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Redefined checkout experience

In an agent-first environment, the concept of a traditional checkout—where a human user confirms their purchase by clicking a “Buy” button—fades into the background. Instead, agents operate with predefined goals and parameters, continuously evaluating whether a proposed transaction aligns with those objectives. Rather than halting everything at a payment prompt, agents could integrate a “stop and reflect” step into their workflows. For example, if a user’s agent is tasked with booking a flight seat that’s both a window seat and close to an exit, the agent pauses before completing the reservation. It double-checks that the seat assignment matches the user’s criteria and only then proceeds, ensuring flawless execution of the user’s intent and mitigating the probability of the agent going off the rails due to hallucinations.

Stripe perfected its checkout experience with its Optimized Checkout Suite, which dynamically shows customers the most relevant payment methods using machine learning, leading to a 10.5% increase in revenue

This reflective process transforms the final authorization into a subtle verification loop rather than a jarring user interruption. The agent reviews the selected attributes—price, seat location, baggage allowance, and cancellation policy—and compares them against the user’s stored preferences and constraints. It confirms not only that the requested outcome has been met but also that it falls within acceptable spending limits and trust parameters. This transforms purchasing from a manual “Are you sure?” prompt into a nuanced, data-driven decision matrix.

As these agent-mediated transactions proliferate, payment providers and merchants might offer additional layers of context-aware validation. For instance, when an agent chooses a specific insurance add-on, the payment system could prompt the agent to confirm whether its logic correctly interpreted the user’s needs. This transparent chain of reasoning, visible to the agent and logged for future reference, ensures that each transaction stands up to scrutiny. Ultimately, the checkout step evolves from a user-facing choke point to an agent-managed quality control measure, minimizing errors and elevating the overall integrity of automated commerce.

As agents reshape how we complete purchases, they're also forcing us to rethink how businesses attract and engage customers in the first place. The era of human-centric marketing campaigns is giving way to something far more systematic and efficient.

Agent-driven marketing and promotions

Marketing campaigns and promotions will evolve radically in an AI agent-mediated economy.

Traditional email marketing and coupon distribution systems, designed around human attention and impulse, will give way to programmatic offer networks where consumers’ AI agents maintain persistent queries about their principals' needs and preferences. These agents subscribe to vendor APIs that broadcast real-time offers matching specific criteria, enabling hyper-personalized deal discovery that transcends the limitations of batch email campaigns.

Vendors might maintain agent-first promotional channels that communicate in structured data formats, allowing instant price comparison and benefit analysis. This ecosystem could enable “intent casting", where agents broadcast shopping goals to trusted vendor networks, receiving precisely targeted offers that align with the principal's timing, budget, and preferences—all without cluttering a human inbox or requiring manual coupon management.

Consider a practical example: A user instructs their agent to monitor high-end fashion retailers for specific items within their style preferences and budget constraints. Rather than the user repeatedly checking websites or subscribing to countless email lists, their agent maintains persistent monitoring across multiple vendors:

• Real-time inventory tracking across size, color, and style variations

• Dynamic price monitoring, including flash sales and member-exclusive discounts

• Evaluation of shipping times and costs to the user's location

When ideal conditions align—perhaps a preferred sweater hits the target price point during an end-of-season sale—the agent can either notify the user or execute the purchase automatically based on pre-authorized parameters. This transforms shopping from an attention-demanding activity into an efficient background process governed by clear rules and preferences.

Major retailers like Nordstrom or ASOS could expose agent-specific APIs that provide structured access to:

• Real-time inventory and pricing data

• Detailed product specifications and measurements

• Membership program benefits and restrictions

• Regional availability and shipping constraints

This evolution democratizes personal shopping, allowing everyone to benefit from persistent, intelligent monitoring of their fashion preferences—not just those who can afford human personal shoppers. It also enables retailers to better match inventory with actual customer intent, reducing overhead from unsold merchandise and improving supply chain efficiency.

Other companies like Honey may need to pivot to offer agent-optimized tools that integrate directly with vendor APIs, allowing agents to query real-time discounts and rewards. Similarly, Mailchimp and HubSpot could develop agent-oriented campaign frameworks that distribute offers as structured data streams rather than traditional email blasts, ensuring seamless integration with agent-driven workflows.

Agent-native customer support

Customer support today centers on human interactions or user-facing chatbots. In an agent-first paradigm, this shifts to agent-to-agent communication. Personalized AI agents will directly engage with business systems to resolve issues, retrieve shipping or refund policies, or autonomously initiate returns. This evolution will streamline processes, reduce human intervention, and enhance efficiency in support workflows.

In an agent-first paradigm, customer support is no longer solely about human users contacting businesses through chat widgets or call centers. Instead, autonomous agents interact directly with enterprise systems, pulling diagnostic information, requesting refunds, or escalating complex issues to a more constrained/expensive resource like a human or a superior model (e.g., o1 over GPT-4o). This shift encourages platforms like Intercom to develop agent-oriented communication layers—specialized APIs that allow autonomous agents to navigate support options, retrieve knowledge base articles, and submit detailed troubleshooting requests without human intervention.

These agent-facing APIs would streamline issue resolution, allowing routine queries, such as package tracking, account verification, or policy clarifications, to be handled agent-to-agent, drastically reducing response times. As soon as a problem arises, the user’s agent can pinpoint the issue and connect with the business’s support agent (be it a specialized LLM or a human representative), negotiating resolutions or applying discounts as needed. The result is a fluid, automated dialogue that bypasses human frustration and latency.

Over time, companies could implement reputation scoring systems that measure how efficiently their support agents (both human and AI) interact with consumer agents. Metrics like resolution speed, policy clarity, and refund accuracy become machine-readable signals, informing user agents which vendors offer superior support experiences. As more vendors embrace these standards, the entire support ecosystem evolves: prompt, well-structured responses become the norm, and agent-native customer support becomes a hallmark of high-quality digital services.

Imagine a complex warranty claim scenario: Your agent detects that your new laptop's battery is degrading unusually fast. It immediately:

1. Collects diagnostic data and usage patterns

2. Cross-references warranty terms with actual performance

3. Initiates a support interaction with the manufacturer's agent

4. Negotiates a resolution based on precedent cases

5. Arranges shipping for replacement parts or full device replacement

6. Schedules a technician visit if needed

This entire process happens without human intervention unless exceptional circumstances arise. The interaction generates a complete audit trail, including all diagnostic data, communication logs, and decision points—valuable data for improving both product quality and support processes.

One example in this space of agentic customer support is Sierra, a startup taking aim at the expansive market of customer support by embedding AI agents into business workflows. Their conversational agents handle complex queries with contextual precision, managing tasks such as processing returns or updating subscriptions. While their primary focus remains on serving human customers, the foundation they’ve built is clearly aligned with an AI agent-driven future. With access to company policies (e.g., refund and shipping rules) and robust conversational AI infrastructure (spanning LLMs and voice interfaces), Sierra is well-positioned to seamlessly transition to support agent-to-agent interactions as demand evolves.

Sierra’s agents resolve support tickets autonomously, with Sierra customers only paying for resolved tickets

The end of language-optimized interfaces

As AI agents seamlessly translate and interpret information on the fly, the need for painstakingly maintained multilingual websites diminishes. Instead of forcing businesses to host separate English, French, or Mandarin versions of their interfaces, agents handle language conversion dynamically. This capability allows brands to maintain a single, streamlined codebase while ensuring that users, regardless of location, receive content and instructions in their preferred language—instantly and accurately.

Website builders like Webflow and Wix could evolve into platforms that generate “universal templates” optimized for agent interpretation rather than human linguistic preferences. Instead of focusing on localized landing pages, these platforms would produce standardized, machine-readable structures enriched with metadata and semantic cues. Agents, armed with cutting-edge language models, would then adapt the presentation layer for each user, including local dialects, cultural nuances, and even personalization cues drawn from the user’s profile.

The transformation goes beyond simple translation. Agents will handle complex cultural adaptations across multiple dimensions simultaneously. They'll dynamically adjust pricing strategies for different markets while modifying product descriptions to reflect local preferences and purchasing patterns. These agents will intelligently adapt imagery and design elements to ensure cultural appropriateness, automatically managing regional compliance requirements such as privacy policies or consumer protection disclosures. They'll even personalize communication styles based on cultural norms, shifting between formal and casual tones and adapting messaging cadence to match local expectations. This comprehensive cultural intelligence transforms what was once a labor-intensive localization process into a fluid, automated system that maintains cultural authenticity across all customer touchpoints.

In this new reality, the value proposition of website builders shifts from localization to robustness, structure, and data integrity. Rather than wrestling with manual translations or commissioning multiple language variants, businesses can rely on well-defined data schemas and agent-ready manifests. As a result, the concept of “language-optimized” sites becomes obsolete, replaced by fluid, dynamic interfaces that transcend linguistic barriers.

Small vs. large business impact

This transition creates both opportunities and challenges across the business spectrum. Large enterprises can invest in building sophisticated agent interfaces and maintaining complex agent-ready APIs. However, small businesses might initially struggle with the technical requirements and infrastructure costs.

To bridge this gap, we will likely see the emergence of "agent-enablement platforms"—services that help small businesses become agent-ready without significant technical investment. Think of them as the Shopify of the agent era, providing standardized tools that level the playing field. These platforms would offer pre-built solutions for creating agent-readable product catalogs and managing automated pricing and inventory systems. They would include standardized support protocols that small businesses can easily implement, along with simplified integration paths to agent payment systems. By democratizing access to agent-ready commerce capabilities, these platforms will play a crucial role in preventing a digital divide between large and small businesses in the agent economy.

This democratization of agent-ready commerce will be crucial for preventing a digital divide between large and small businesses in the agent economy.

The Great Rewiring

The transition to an agent-first internet represents more than just a technological shift—it's a fundamental reimagining of how commerce functions in the digital age. We're moving from a web optimized for human attention and interaction to one built for efficient, automated decision-making. This transformation touches every aspect of online business:

• Payment systems evolve from human-verification models to agent-oriented protocols with built-in delegation and accountability

• Marketing shifts from attention-grabbing campaigns to structured, machine-readable offer networks

• Customer support transforms from human-to-human interaction to efficient agent-to-agent problem resolution

• Language barriers dissolve as agent-mediated communication enables seamless global commerce

Companies that quickly adapt to this new paradigm—implementing agent passports, embracing agent-to-agent protocols, and restructuring their services for machine readability—will shape the next era of online interaction. Just as the mobile revolution created trillion-dollar opportunities, the agent revolution opens new horizons for innovation and value creation. The businesses that thrive won't just be those with the best products or prices, but those that best enable and embrace agent-driven commerce.

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This is the third essay in a five-part series exploring the future of AI agents and their impact on the internet. Having established the foundational shifts and their practical implications across multiple domains, our next post will examine early case studies and implementation challenges as businesses begin this transformation. Subscribe and follow as we continue to navigate the technical, social, and economic implications of this new paradigm.