Large language models (LLMs) and agentic AI are changing the way people shop, interact, and even complain, often without leaving a traditional interface.
For contact centers, this evolution brings a mix of opportunities and urgent challenges. These are faster, more seamless experiences for consumers. But they also pose new risks that can drive sudden spikes in inbound contacts, disputes, and customer frustration.
The rise of AI-powered shopping agents, from chatbots that complete purchases to LLMs connected to merchant catalogs, is accelerating what the industry calls “agentic commerce.”
But without careful planning, the technology can amplify the negative impacts of fraud and policy abuse...
These systems can browse, compare, and buy on behalf of users. But in doing so, they often compress or eliminate the digital breadcrumbs that fraud and risk teams have relied on for years.
This leaves contact centers open to fraud and can lead to costly customer service and operations stress and disruption.
The Hidden Tech Challenge
From both product and risk perspectives, AI-enabled purchases are fundamentally different.
Traditional fraud detection relies on patterns like browsing time, hesitation between clicks, device switching, or even straightforward data points such as IP addresses or email addresses.
But agentic AI purchases can compress or omit many of these signals entirely, creating blind spots. A single AI transaction may look almost instantaneous, leaving no record of hesitation, no pattern of repeat visits, and often limited or anonymized user data.
For fraud teams, this is more than a theoretical problem. The missing data can lead to increased chargebacks, higher dispute rates, and more work for contact center staff.
Without these signals, distinguishing legitimate customers from attackers becomes more difficult, forcing teams to balance approving transactions to avoid friction while mitigating potential losses.
The Human Impact
Contact centers are often the first to notice the ripple effects of agentic AI.
When a consumer notices a transaction they don’t recognize or when a reseller exploits AI to generate multiple orders, the contact center absorbs the fallout. Calls and chats increase, agents must troubleshoot with limited data, and the pressure on customer satisfaction grows.
This surge can be especially disruptive because agentic AI accelerates the pace at which these issues appear.
With AI-assisted transactions, thousands of purchases could occur almost simultaneously, potentially resulting in sudden spikes in purchase issues (e.g., wrong items delivered or duplicate orders) and customer inquiries.
But without new tools and protocols, contact centers can be overwhelmed, and customers may experience delayed or inconsistent responses.
Fraud and risk teams will struggle even harder to catch up; traditional eCommerce fraud patterns tended to evolve more gradually than the rapid bursts that may occur with AI-powered shopping agents.
AI-Driven Risk Detection, Prevention, Mitigation
Despite these challenges, organizations can take proactive steps to adapt.
1. Understand data shifts. Fraud and risk teams must recognize that agentic AI changes the basic signals available for detecting suspicious activity. Traditional heuristics — hesitation, page dwell time, switching devices — may be compressed or missing.
Contact center teams should be trained to recognize scenarios where AI-assisted transactions are more likely and where standard troubleshooting and verification workflows may need adjustment.
2. Implement smarter intelligence platforms. To compensate for missing signals, businesses can deploy AI-driven fraud intelligence platforms that aggregate data across merchants and transactions.
These platforms can identify patterns across multiple AI-assisted transactions, helping restore the visibility that individual merchants lose. For contact centers, this means clearer guidance on which disputes are likely genuine and which are part of coordinated abuse.
3. Share responsibility across teams. Risk mitigation cannot fall solely on fraud teams. Cross-functional coordination between product, risk, and customer support is essential.
Contact center staff need clear protocols for escalating suspicious cases, while fraud teams refine approval rules and thresholds to minimize both chargebacks and customer friction.
4. Prepare the organization for AI-driven spikes. AI doesn’t just change transactions; it changes volumes.
Contact centers should anticipate periods of high demand, potentially driven by automated purchasing flows, and plan staffing, technology tools such as AI-assisted agents, escalation procedures, and customer messaging accordingly.
Educating executives and operational leaders about these new patterns is critical to ensure timely support and realistic expectations.
Balancing Risk and Opportunity
The potential upside of agentic AI is enormous: faster discovery, simplified purchases, and more personalized experiences. But without careful planning, the technology can amplify the negative impacts of fraud and policy abuse, leaving contact centers and customers to bear the brunt.
For example, resellers exploiting AI to purchase large quantities of limited inventory may not technically commit fraud. But the effects are similar: customer frustration, inventory strain, and an influx of service tickets.
Ultimately, the organizations that succeed in this new era will be those that anticipate the technology’s disruptive effects...
Similarly, a compromised AI account could generate dozens of orders in minutes, creating a sudden wave of inbound disputes and refunds. In both cases, contact center teams are on the front lines, often lacking sufficient context to resolve the issues efficiently.
Integrating agentic AI considerations into contact center workflows, risk strategies, and product design is no longer optional.
Organizations that can detect unusual AI-driven patterns, automate parts of dispute resolution, and provide agents with actionable insights will not just mitigate risk. They will also maintain customer trust and satisfaction.
Looking Ahead
The evolution of eCommerce agentic AI is still in its early stages. Vendors and researchers continue to explore frameworks like OpenAI’s Agentic Commerce Protocol, which promises to connect AI assistants to merchant catalogs and checkout flows.
While these systems may streamline shopping, they also reduce the visibility of underlying transactions, requiring merchants and contact centers to rethink their approach to risk and customer support.
Ultimately, the organizations that succeed in this new era will be those that anticipate the technology’s disruptive effects, equip their teams with transaction and risk data along with AI-driven tools and insights, and adapt workflows to both mitigate risk and preserve customer experience (CX).
For contact centers, this means balancing speed and security, leveraging AI to support staff rather than overwhelm them, and treating AI-driven transactions not as anomalies but as the new normal.
Agentic AI is changing the rules. By understanding the technical shifts, anticipating spikes in consumer inquiries, and integrating fraud and contact center strategies, businesses can navigate this transition with confidence: keeping both risk and customer satisfaction under control.