The Rise of Agentic AI in Contact Centers

Agentic AI is rapidly becoming part of how contact centers operate, reshaping how calls are handled and how inbound interactions are initiated. Within organizations, it’s being used to automate customer interactions and offload repetitive work. Outside the organization, agentic voice agents now place calls to gather information or take action on behalf of individuals or systems.

Unlike traditional automation and IVR systems that simply react to prompts or follow rigid scripts, agentic AI operates with autonomy, deciding what to ask next, when to persist, and how to adapt based on responses. This shift is not only changing how calls are handled but who, or what, is initiating them externally.

In this post, we look at how agentic AI is being adopted across contact centers, why it behaves differently from legacy IVR systems, and the operational considerations leaders should address as inbound AI interactions scale.

What Is Agentic AI?

Agentic AI refers to autonomous AI systems that can plan, make decisions, and take action to achieve a goal without constant human oversight. Unlike scripted automation, these systems decide what to ask, how to respond, and when to persist, change approach or escalate. Over time, these systems can learn from outcomes and refine how they pursue similar objectives.

The defining characteristic of agentic AI is execution, not conversation. Its purpose is to complete work, not just generate responses.

Agentic AI in the Contact Center

Agentic AI is changing how contact centers think about automation and scale. Rather than serving only as an interface layer for routing or deflection, it introduces a new execution model—one where software systems can take responsibility for completing customer requests, not just passing them along.

This shift has implications for efficiency, staffing models, and how inbound interactions are handled, especially as both enterprises and external parties begin using autonomous voice agents at scale.

How Agentic AI Differs from IVRs, Bots, and Traditional Automation

IVR

Traditional interactive voice response (IVR) systems are built around menu trees and keyword matching. They’re effective at routing calls or collecting basic information, but they struggle when callers deviate from expected paths. Any complexity typically results in a transfer to a human agent, increasing handle time and operational cost.

Chatbots and early voice bots

Chatbots and early voice bots introduced natural language understanding, allowing callers to speak more freely. However, these systems remain largely reactive. They respond to prompts, follow scripted flows, and rely on predefined integrations. When a request falls outside those boundaries, escalation to a human agent is still required.

Agentic AI

Agentic AI operates differently inside the contact center. Instead of reacting to individual prompts or following fixed flows, it works toward completing an outcome across the entire call. Agentic voice agents can determine what information is needed, decide next steps and interact with backend systems, such as CRM platforms, eligibility databases, or scheduling tools. They can be used for scheduling appointments, checking eligibility, updating records, or resolving service requests.

Because agentic systems are designed to persist until a task is complete, they reduce unnecessary transfers and handoffs. Routine interactions can be handled autonomously over the phone, with escalation to human agents only when complexity, exceptions, or uncertainty arise. This shifts automation from call routing and deflection to true resolution, improving efficiency and lowering average handle time.

When Agentic AI Becomes the Caller

At the same time, agentic AI is increasingly acting as a caller itself. Agentic voice agents now place inbound calls to contact centers on behalf of individuals, providers, or systems, requesting information, confirming details, or taking action. These AI callers sound human, adapt in real time, and persist across interactions, introducing new challenges for routing, efficiency, and control when they arrive inbound at scale.

How Enterprises Are Using Agentic AI in Contact Centers

Agentic AI is already in early production across several regulated, high-volume contact centers. The common driver is the need to manage rising inbound demand, reduce handle time, and improve operational efficiency without adding headcount.

Healthcare and Health Insurance Contact Centers

Healthcare organizations and health insurance providers are among the earliest adopters of agentic AI for inbound communications, particularly for appointment scheduling, patient intake, coverage verification, and provider servicing, workflows that have historically driven long handle times.

Platforms such as Cognigy and Kore.ai are being deployed in large healthcare contact centers to power agentic voice agents that integrate directly with scheduling systems and insurance databases, enabling these calls to be resolved end to end without human involvement.

At the same time, large technology providers and systems integrators are also enabling healthcare organizations to build custom agentic voice agents on modern conversational AI platforms, targeting high-volume inbound calls that consistently strain contact center capacity.

While these use cases are strong candidates for automation, deploying agentic systems to manage them remains at an early stage and requires careful risk oversight as the technology matures.

Financial Services and Insurance

For financial services and insurance organizations, adoption is accelerating. United Kingdom based banks, including NatWest Group, Lloyds, and Starling, are preparing customer-facing and agent-facing artificial intelligence trials designed to accelerate call outcomes by surfacing relevant information and next best actions during live interactions.

The Financial Conduct Authority’s Chief Data Officer Jessica Rusu has said she expects early consumer-facing agentic AI applications to reach the market early in 2026, noting in comments to Reuters that agentic AI introduces new risks because of its ability to act at pace.

In the United States, large banks, including JPMorgan Chase, are currently using agentic AI primarily in back-office operations to automate complex, multi-step work such as document preparation, internal analysis, and process execution for employees. This reflects a broader industry trend; organizations are gaining confidence in autonomous systems internally before extending them to customer-facing contact center environments. According to Gartner, 40 percent of financial services firms are expected to use AI agents by the end of 2026.

Telecommunications

In telecommunications, conversational artificial intelligence platforms are being used by providers, including AT&T, to support customer service workflows such as service updates and account changes, with agentic systems executing multi-step tasks behind the scenes under human oversight. These deployments closely mirror trends in healthcare, where sustained, high-volume inbound demand places continuous pressure on staffing levels, service quality, and operating costs.

The Benefits and Risks of Agentic AI

Agentic AI changes the economics of contact center operations by automating high-volume, low-complexity interactions end-to-end. By resolving routine inquiries without human involvement, organizations can reduce average handle time, lower cost per call, and improve overall service efficiency.

Because agentic AI systems operate autonomously, they enable round the clock availability without linear increases in headcount. Unlike traditional automation that relies on rigid scripts, agentic AI can reason through variations in requests, helping contact centers scale capacity while maintaining operational control and more predictable costs.

However, increased autonomy also introduces new operational risk. Agentic AI is powerful when deployed intentionally to support contact center operations. But when inbound AI callers begin arriving at scale, the same capabilities can drive unexpected cost and inefficiency.

Without a reliable way to understand traffic patterns between human callers, internally deployed agents, and inbound AI callers, contact centers may struggle to route interactions correctly or understand where agent time is being consumed. As inbound AI traffic grows, this can lead to misrouted calls, inflated handle time, and uncontrolled operational load, even before fraudulent use enters the picture.

What Comes Next for Agentic AI in Customer Service

As artificial intelligence becomes more widely used by both contact centers and the organizations or individuals calling into them, contact centers will need to define how they handle this shift. As inbound traffic begins to include more autonomous callers acting on behalf of others, organizations will have to determine whether to accept agentic callers, how to detect them, and how to manage volume as usage grows. Reality Defender provides a solution to help detect and classify AI-generated audio at scale to support operational oversight and trust.

The next phase of adoption will favor organizations that treat agentic AI as an operational system rather than an experiment. For large contact centers, the opportunity is to use agentic AI to improve efficiency and scale inbound communications while maintaining control over cost, routing and service quality.