A practical guide from Ben, Chargeflow’s Fraud and Chargeback Strategist and Mentor at the MRC

Agentic Commerce Is Rewriting the Risk Playbook

AI agents are not only affecting purchases, but they are also starting to make them. As these automated systems handle more of the buying process, new problems arise in how merchants detect fraud, understand customer intent, and avoid chargebacks. 

I see this change every day. Dispute data reveals a rising trend of customers disputing charges that resulted from automated decisions they did not fully understand or expect. Through my work mentoring fraud teams, participating in the MRC’s Fraud Committee, and collaborating daily with merchant operations, I observe how both customers and fraudsters are adjusting to this new way of shopping. 

This guide focuses on issues already appearing in dispute queues, rather than predictions about the distant future. These cases show how quickly agent-driven purchasing is shaping post-purchase risk. 

What Agentic Commerce Changes About Fraud

Traditional fraud tools assume a human is behind every action. That assumption begins to falter the moment the buyer is an autonomous agent.

Early in the section, I place the following industry quote because it frames the shift clearly:

“AI agents are not just assistants — they are becoming decision makers in commerce, and the payments infrastructure designed for humans must be rethought.” — Visa

As agents gain greater control, many of the longstanding signals lose their reliability. Device fingerprints, behavioral profile patterns, friction signals, and navigation paths are rooted in human-based behaviors. Agents are now acting in predictable and "machine-consistent" ways that existing systems cannot process.

Intent becomes much harder to verify. Sometimes the customer expected the agent to take action, but not in the specific manner in which it was taken. Sometimes the agent acted on learned logic that the customer had overlooked or forgotten. Fraudsters are leveraging that lack of clarity. Legitimate customers are, at the same time, disputing charges because of their own confusion versus deliberate misuse.

The risk extends far beyond fraud. There is increasing misalignment between what customers expect and what their agent decides.

3. Real-World Scenarios Ben Is Already Seeing

Among merchants using agent-driven purchasing, several consistent patterns are emerging. They appear in dispute reports, customer support threads, and fraud reviews, reflecting how quickly agentic commerce is changing the structure of post-purchase risk. This shift also aligns with recent Money20/20 insights on how AI-driven purchasing is already altering fraud and customer intent.

‍

Here are a couple of examples:

A. AI Purchases the Customer Cannot Recall

Many merchants are already experiencing disputes related to orders that were technically authorized but not consciously requested by the customer. An agent may reorder items based on past behavior, availability, or preference learning. Still, the customer may not remember granting that approval or may never notice the automation running in the background.

When the charge appears, the customer's instinct is to deny any involvement. Even though the order is legitimate, the merchant cannot easily demonstrate intent because the human did not complete the transaction. The Agent did. 

In most cases, confusion directly results in a chargeback.

B. Delegated Mistakes

Agents are designed to optimize, not to interpret human context. They may choose a slightly different product than expected, select a merchant the customer wouldn't normally use, or buy the wrong quantity based on how they analyzed the prompt or data.

Instead of contacting support, many customers go directly to their issuer when the agent’s choice falls outside their expectations. The dispute becomes the customer’s way of correcting what they see as an error, even though the transaction was technically valid from the agent's perspective.

C. Abuse of Automation APIs

Fraudsters now understand that automated traffic blends in considerably better than human or manual traffic. By spoofing agent patterns or exploiting automation endpoints, they can generate transactions that appear structured, consistent, and low-risk to legacy fraud systems.

Because these flows bypass many human indicators, to the merchant, the activity appears normal and in line with legitimate automation. Only after the dispute is filed does the pattern reveal itself as synthetic. This approach is gaining traction because it passes through the gaps created by agent-driven workflows.

D. False Positives Creating Future Chargebacks

Some merchants are encountering disputes that originate from friction created within the fraud stack, rather than with fraud itself.

Agent-driven flows sometimes trigger fraud rules, require extra verification, or get declined. The customer is then confused and frustrated with the process, especially when the agent handled the flow "out of sight." Later, when a legitimate charge appears, the customer disputes it simply because their trust in the flow was already broken.

These are entirely preventable, but they show how easily agentic behavior and human expectations fall out of sync.

4. Why Legacy Fraud Tools and Playbooks Break Down

Most pre-payment fraud systems and tools were developed during a time when a human was involved in every step of the customer journey. Agent-driven commerce breaks that foundation, which means some of the strongest tools in a merchant’s stack no longer behave as expected.

Device-based risk models lose meaning.

Traditional models rely heavily on device characteristics to identify suspicious behavior. When the “buyer” is an agent running through servers or cloud environments, the device attributes no longer map to human identity or intent. This removes a major anchor point in existing fraud detection logic.

Velocity rules begin to misclassify automated flows.

Agents often operate on schedules or logic loops that repeat with predictable timing. Legacy velocity rules (e.g., based on the actual hour of the activity) are designed to flag repeated human behavior. They incorrectly escalate normal agent activity, creating false positives that lead to friction, missed revenue, and downstream disputes.

Behavioral analytics cannot interpret non-human patterns.

Models that rely on mouse movement, scrolling, pause time, or speed between actions lose effectiveness because agents do not adhere to human interaction norms. What appears suspicious in a human context may be entirely legitimate when an agent is executing the flow.

Manual review becomes unmanageable.

Agent-driven transactions increase volume while reducing visibility. Cases that once required a few minutes of analysis now lack the human signals reviewers rely on. Manual review cannot scale with automation, and even when teams attempt to do so, the outcomes are inconsistent because the underlying signals are incomplete.

The biggest gap appears during disputes.

Even when a merchant knows a transaction is legitimate, proving intent becomes significantly harder. Issuers expect evidence that shows a clear connection between a customer and a purchase. In agentic commerce, part of that action is delegated. Without new types of supporting data, merchants lose cases simply because the evidence cannot resolve the dispute.

Legacy systems are not failing because they are weak. They are failing because they were never designed for environments where agents, not humans, complete major parts of the purchase journey.

5. The Move to Post-Purchase Intelligence (Ben’s POV)

As agent-driven commerce grows, the signals that matter most often appear after the transaction, not before it. Pre-purchase controls were designed to address human behavior and struggles when intent is shared between a human and an agent. In this environment, preventing losses requires stronger visibility into what becomes clear only once the order exists.

Post-purchase intelligence fills in the gaps that legacy systems leave behind. It helps answer questions that cannot be resolved at checkout, including:

• Does this order make sense given the customer’s past behavior, or does it indicate confusion or unintended automation?
• Does this agent have recognition across a wider merchant network, or is it new, unverified, or behaving inconsistently?
• Have similar transactions at other merchants resulted in disputes that signal misuse or new fraud techniques?
• Are there inconsistencies in identity or device attributes that suggest manipulation behind the automation?

These signals offer context that pre-purchase tools cannot. They help determine when an agent acted outside customer expectations, when automation is being abused, and when a familiar pattern is likely to convert into a dispute.

Until today, in many cases, the only reliable way to understand intent well enough to intervene before the issuer becomes involved has been post-purchase analysis. It gives merchants a chance to reach out, verify, correct mistakes, or cancel orders before they become chargebacks, and for physical goods, before the products are shipped.

Post-purchase intelligence is more than an upgrade. It is a necessary shift in how merchants protect revenue in an environment where agents, not humans, initiate more of the buying process.

6. How Chargeflow Prevents and Supports Merchants in Agentic Commerce

Although this guide is not focused on products, it is important to recognize that merchants need tools that reflect the realities of agent-driven purchasing. Automation introduces gaps that traditional fraud systems were never meant to handle, and many merchants are looking for practical ways to close those gaps without adding friction or manual effort.

Chargeflow Prevent was built with this environment in mind. It uses post-purchase scoring, network-level intelligence, and identity and agent recognition to help merchants understand when a transaction is likely to lead to a dispute. These signals provide context about the behavior behind an order, including patterns that never appear during checkout.

This approach supports the shift from human-centric fraud modeling to agent-centric risk evaluation. It helps merchants separate legitimate automation from misuse, identify early signs of confusion or unintended purchases, and intervene before a charge becomes a chargeback.

My goal is not to promote a specific solution, but to emphasize that merchants now need preventive layers that reflect how commerce is changing. Agent-driven transactions require a different type of visibility, and tools like Chargeflow Prevent are designed to provide that visibility in a practical and actionable way.