De-risking Investments in Agentic AI: Practical Paths to Safer CX

Agentic AI is changing customer experience

Automation now underpins many customer interactions — from chatbots to recommendation engines. The next wave, often called 'agentic AI', moves beyond scripted flows: these systems can plan, act, and adapt toward goals. That flexibility promises richer, more human-like experiences, but it also introduces new uncertainties for businesses that must deploy these systems at scale.

The business opportunity and its trade-offs

AI agents can handle complex service interactions, support employees in real time, and scale as demand shifts. For companies, that means potential cost savings, faster resolution times, and more personalized service. At the same time, non-deterministic behavior complicates testing, auditing, and risk management. Questions surface: how do you test a system that may not give the same answer twice? How do you protect core infrastructure when agents need access to live systems? How do you measure ROI when outcomes are probabilistic?

Neeraj Verma, vice president of product management at NICE, captures the shift in expectations: 'Every single person that I’ve spoken to has at least spoken to some sort of GenAI bot on their phones. They expect experiences to be not scripted. It’s almost like we’re not improving customer experience, we’re getting to the point of what customers expect customer experience to be.' Verma argues that companies focusing on outcome-oriented design and applied use cases will be among the winners as agentic AI matures.

Key risks when adopting agentic AI

• Testing and validation: Non-determinism makes traditional QA approaches insufficient. Regression testing, deterministic mocks, and scenario-based evaluations need to be rethought.
• Safety and guardrails: Agents with broad capabilities can take unsafe actions if not constrained. Access control, policy enforcement, and runtime monitoring are essential.
• Infrastructure and data access: Granting agents privileges to act on backend systems increases operational risk; secure sandboxes and least-privilege models are required.
• Cost and efficiency: Generative systems can be resource-intensive. Without cost controls and observability, projects may exceed budgets.
• Transparency and ethics: Explainability, audit trails, bias mitigation, and compliance become core requirements as agents influence customer outcomes.

Practical strategies to reduce risk

• Outcome-oriented design: Start with clear business outcomes and success metrics. Design agents to optimize those measurable outcomes rather than mimic human scripts.
• Progressive sandboxing and scoped access: Test agents in simulated environments, then grant incrementally broader capabilities with strict monitoring and rollback plans.
• Layered guardrails: Combine static policies (rules, input sanitization) with dynamic checks (runtime monitors, anomaly detection) and human-in-the-loop interventions for high-risk decisions.
• Robust testing frameworks: Use scenario-based testing, fuzzing, adversarial inputs, and long-running behavioural tests to expose failure modes that single-run tests miss.
• Observability and auditability: Instrument agents with logging, traces, and explainability outputs so decisions can be reconstructed and audited.
• Cost governance: Implement throttling, usage budgets, and model selection policies to control spend while optimizing for quality of service.
• Phased rollouts and pilot programs: Deploy agents on targeted, low-risk use cases first, gather metrics, and iterate before broader release.
• Vendor and tooling selection: Prefer vendors and platforms that offer role-based access control, audit logs, and built-in safety features, and that align with your compliance needs.

Measuring success

Shift KPIs from narrow accuracy metrics to outcome-based measures: customer satisfaction, resolution time, error rates, escalation frequency, and cost per interaction. Monitor for degradation over time and tie model and policy changes to measurable business impacts.

Who benefits most

Applied AI companies and teams that focus on specific use cases — not just general-purpose models — are likely to see the largest returns. Organizations that combine technical guardrails, clear outcome orientation, and operational rigor will move fastest and safest into the agentic AI era.

This piece was produced by Insights, the custom content arm of MIT Technology Review, and developed by human writers and editors with limited AI-assisted production support.