AI agents are rapidly becoming part of the operational fabric of customer experience (CX) solutions, used to resolve issues, execute workflows, and interact directly with customers across digital channels.
A recent Cisco survey found that 68% of all customer service and support interactions with technology vendors are expected to be handled by agentic AI by 2028, for example.
As organizations deploy more agents across teams and functions, the risks escalate, including inconsistent decision-making, regulatory exposure, and the potential loss of customer trust.
The remedy is strong governance, knowledge controls, and evaluation frameworks. However, organizations must implement them mindfully, as overly restrictive controls can push teams toward using unsanctioned systems, accelerating the rise of shadow AI.
Consequences of AI agents failures bad
Consider this solution: Sprinklr has developed an AI-powered platform that helps enterprises manage customer-facing functions. Sean Brownell, solutions director for strategic accounts at Sprinklr, sees a common challenge emerging as organizations deploy AI agents.
“AI agents can be confidently wrong,” says Brownwell. “A confused human agent says, ‘Let me check on that.’ An ungoverned AI agent says, ‘Absolutely, here’s your answer,’ and moves on. That gap between certainty and accuracy is where trust dies.”
Those inaccurate answers can result in real-world consequences, including financial repercussions, such as applying incorrect discount codes or refund policies, Brownell says. In regulated industries, the result can be inaccurate product information that creates a compliance liability. Reputational damage is another risk, particularly when a bizarre or offensive response from an AI agent goes viral.
Perhaps the most insidious consequence is the operational burden that follows: repeat customer contacts, escalations, and manual remediations to fix problems that nobody attributes to an AI failure. “This is the cost that most organizations discover too late,” he says.
Finding the AI governance balance
It may be tempting to enforce strong governance rules defining AI use cases and who can use which specific AI tools. But the more you enforce strict rules, the more likely teams are to route around them, resulting in shadow AI, Brownell says.
“Employees will use personal accounts and consumer tools to do their jobs, entirely outside the enterprise perimeter,” he says. “You haven’t solved the risk; you’ve just lost visibility of it.”
On the other hand, governance that’s too lax can result in AI agent sprawl, with “disconnected bots deployed across teams with no centralized oversight,” Brownell says. “Both paths lead to the same destination: exposure.”
Such agent sprawl can also make it difficult to comply with regulations such as the EU AI Act, which requires AI applications to be explainable, auditable, and subject to human oversight.
The goal is to make safety a critical component of the system, part of its architecture rather than a gate that’s always in the way.
Three principles for proper AI governance
To strike the right level of governance, Brownell advises companies to adopt three principles.
“First, own the knowledge layer,” he says. Most AI agent failures trace back to outdated information, conflicting sources, or out-of-context data. “Getting control of the knowledge base that feeds your agents is the highest-leverage governance intervention available, and it doesn’t require rebuilding your technology stack.”
This means employing a solution that can capture every conversation across all CX channels. “That matters for governance because it ensures every agent, every interaction, and every piece of knowledge lives within a coherent, auditable structure,” Brownell says.
Second, design for humans-in-the-loop from the start, not as an afterthought. Human oversight should be a feature of the system rather than a fallback when AI gets stuck. “Defining the cases where AI always escalates, not just the cases where it’s allowed to, gives you confidence to expand AI scope elsewhere,” he says.
Lastly, measure the right things, including accuracy, consistency, regulatory adherence, and outcome quality. Doing so will not only help you manage risk, but also create the foundation for board-level conversations that justify your next phase of investment,” Brownell says.
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