Agentic AI and Chargebacks: New Risks for Merchants in 2026

What Are Agentic AI Systems?

Agentic AI systems are artificial intelligence applications that operate with some degree of autonomy — taking actions in the world based on high-level user instructions rather than explicit per-action approval. Unlike a chatbot that answers questions, an agentic system might browse the internet, fill in forms, make reservations, and complete purchases as part of fulfilling a user's request.

Examples in 2026 include: AI assistants that book travel based on a user's stated preferences, shopping agents that find and purchase products on behalf of users, subscription management tools that sign up for and cancel services autonomously, and business AI that procures supplies based on inventory rules.

For payments specifically, agentic AI typically uses stored payment credentials — the user's credit or debit card — to complete purchases. The user may have set up the AI to use their card for purchases up to a certain amount, or within certain categories, without requiring per-purchase approval.

This creates a new layer of authorization complexity that the existing card network dispute framework was not designed to address.

How Agentic AI Creates Chargeback Risk

The core chargeback risk from agentic AI stems from a potential mismatch between what the user intended when they authorized the AI to make purchases and what the AI actually purchased.

Scope creep disputes: a user authorizes an AI to "buy office supplies" up to $100. The AI interprets this broadly and purchases $95 of items the user considers out of scope. The user disputes the charge, claiming they didn't authorize this specific purchase.

Unauthorized purchase disputes: a user's AI agent makes a purchase the user later decides they don't want. The user may attempt to dispute it as "unauthorized" even though they authorized the AI to make purchases on their behalf.

Compromised agent disputes: if a user's AI agent is compromised (hacked, manipulated through prompt injection, or otherwise acting outside the user's instructions), purchases made by the compromised agent may legitimately be unauthorized from the user's perspective.

For merchants receiving payments from AI agents, none of these distinctions are visible. The payment appears to come from a valid card with matching credentials. There is no signal that an AI made the purchase rather than the human cardholder.

The Authorization Question in AI Purchases

Card network rules on authorization assume that a human cardholder either authorized a transaction or didn't. The concept of delegated authority — where a cardholder authorizes an AI to make purchases within defined parameters — exists in corporate card programs (where companies authorize employees to spend within policy) but has no formal framework in consumer card networks.

This legal and procedural gap means that disputes arising from AI agent purchases are likely to be treated as standard authorization disputes. If a cardholder files an "unauthorized transaction" claim for a purchase their AI agent made, the issuing bank may side with the cardholder regardless of whether the AI acted within its authorized scope.

For merchants, this means transactions made by AI agents — even when the cardholder previously authorized the agent — carry some chargeback risk if the cardholder later disputes the specific purchase. The merchant's evidence (the transaction was authorized, the card credentials were valid) may be insufficient if the cardholder argues the AI exceeded its authorization.

Card networks are aware of this emerging issue and are working on frameworks to address delegated authorization. Until those frameworks exist, merchants face uncertainty.

Practices to Reduce Agentic AI Chargeback Risk

Several merchant practices reduce exposure to agentic AI chargeback risks, even while the broader framework remains unsettled.

Obtain email confirmation for AI purchases: if you can detect that a purchase is being made by an AI agent (through API flags, user-agent strings, or behavioral signals), sending an email confirmation to the human account holder before completing the transaction adds a human checkpoint. A human who confirms the purchase cannot later claim it was unauthorized.

Implement purchase confirmation steps for unusual orders: for orders that deviate from the account holder's typical purchase pattern (different category, higher value, new shipping address), requiring human confirmation adds a layer of authorization evidence.

Maintain detailed transaction logs: for any transaction where AI agent involvement is possible, log all available contextual data — the device type, user agent string, API credentials, session characteristics. This data may be relevant to dispute evidence as AI-related disputes become more common.

Monitor for AI agent fraud patterns: malicious actors are beginning to use AI agents to make fraudulent purchases at scale. Velocity rules, behavioral analytics, and anomaly detection that can identify non-human purchase patterns help flag potentially fraudulent AI-driven transactions.

What to Expect as AI Purchases Become Mainstream

The volume of purchases made by AI agents is expected to grow significantly over 2026 and beyond. As this happens, the dispute rate on AI-made purchases will become a measurable commercial issue for merchants and card networks alike.

Card networks are likely to develop formal frameworks for delegated authorization — similar to corporate card programs — that allow cardholders to explicitly authorize agents (human or AI) to make purchases on their behalf with appropriate liability rules. Until these frameworks exist, merchants should treat AI agent purchases as elevated-risk transactions.

Consumer education will be important. Many users who authorize AI agents to make purchases don't fully understand the card network implications — that using their card through an AI doesn't give them the same "I didn't authorize this" protection they'd have if their card was stolen.

For merchants, staying informed about AI payment developments through card network announcements, acquiring bank communications, and industry publications is important. The dispute landscape for AI-made purchases will evolve rapidly over the next 12–24 months.

ChargeMate monitors developments in AI-related dispute patterns and will update our dispute management strategies as the landscape evolves.