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10/07/2026 9:16 pm
TL;DR: Agentic AI is changing online shopping by offloading research, deal hunting, cart building and even higher-stakes purchase tasks, while merchants start seeing shorter sessions, thinner device signals and more AI-chat referrals, according to Signifyd. The governance challenge is no longer whether agents buy, but how to distinguish legitimate agent-led commerce from automation that breaks legacy fraud models.
NHIMG editorial — based on content published by Signifyd: Why Agentic AI? How to Build Trust in the New Commerce Journey
By the numbers:
- 38% of U.S. consumers are already leveraging AI for help with their online shopping tasks.
- 50% of all Cyber Week orders in 2025 included a discount code, signaling heightened consumer demand for value and increasing agent activity.
Questions worth separating out
Q: How should merchants distinguish AI agents from fraud bots in ecommerce traffic?
A: Merchants should combine behavioural analytics with device trust, transaction history and policy scope rather than relying on browsing depth alone.
Q: Why do AI agents complicate ecommerce fraud detection?
A: They complicate fraud detection because they remove the browsing patterns legacy models were built to trust.
Q: What breaks when merchants treat agent-led shopping like normal human browsing?
A: The main failure is signal misclassification.
Practitioner guidance
- Define delegation boundaries for agent-assisted shopping Map which shopping tasks can be completed by an AI agent without further confirmation and which require an explicit human decision before checkout, subscription changes or returns.
- Retune fraud models for agent-like behaviour Update detection logic to account for short sessions, direct PDP access, thin device identifiers and rapid checkout patterns so legitimate agent-led commerce is not flagged as hostile automation.
- Add identity context to transaction scoring Combine customer history, device reputation, session consistency and policy scope so the platform can tell a customer-authorised agent from an unauthorised bot.
What's in the full article
Signifyd's full blog post covers the operational detail this post intentionally leaves for the source:
- Signifyd's examples of the shopping signals merchants can use to detect agent-led behaviour in live analytics.
- Its breakdown of consumer trust progression from low-risk shopping tasks to higher-stakes delegated purchasing.
- The merchant-side implications for fraud review, checkout decisions and false-positive handling when automation looks human enough to pass basic filters.
- The specific commerce scenarios where agentic AI may shift from assistance to payment and returns execution.
👉 Read Signifyd's analysis of how agentic AI is changing ecommerce trust and fraud signals →
Agentic commerce and fraud signals: what merchants need to know?
Explore further
10/07/2026 9:39 pm
Agentic commerce is becoming a delegated identity problem, not just a fraud problem. The article focuses on shopper convenience, but the security implication is that software is increasingly acting with purchase authority on behalf of a person. That changes the boundary of trust, because the merchant is now judging an action chain rather than a human-only session. For IAM and fraud teams, the question becomes how to govern delegated action without collapsing customer identity into automation heuristics.
A question worth separating out:
Q: Who is accountable when an AI agent completes a purchase the customer did not intend?
A: Accountability sits across the merchant, the platform and the customer, but the merchant still owns the policy that decides whether a transaction is accepted. Teams need clear rules for confirmation, chargeback handling and delegated scope, because once agentic action reaches payment or returns, governance becomes a transaction-control issue as much as an identity issue.
👉 Read our full editorial: Agentic commerce is reshaping trust and fraud signals in ecommerce
