Over the past decade, technological adaptation has been a constant in customer service. That progress has often come through incremental fixes—layering chatbots, scripts, and digital workflows onto legacy systems never built for today’s omnichannel demands. Those layers helped enable AI automation in contact centers, but they also introduced new complexity through disconnected data and limited visibility across customer journeys.
In recent years, conversational AI emerged to make those interactions feel more natural and contextually aware, helping customers self-serve while assisting agents in real time. Now, customer experience (CX) leaders are looking to move further toward true intelligence, simplifying operations while creating more responsive, personalized experiences. The latest research shows that spending on contact center AI is projected to grow nearly 24% annually, reaching about $7 billion worldwide by 2030.
Generative AI (GenAI) has been the primary focus of this investment, enabling more dynamic conversations, real-time summarization, and knowledge retrieval. But GenAI is still reactive and dependent on human prompts. The next evolution is agentic AI, which acts on goals and intent to drive outcomes autonomously. It represents a critical shift, and understanding the distinction between agentic AI vs. GenAI in contact centers will be essential for staying ahead in the next era of proactive customer support.
The benefits and limits of GenAI in CX
It’s difficult to overstate the impact of GenAI on the way contact centers interact with customers and manage information. Built on large language models (LLMs), GenAI powers more natural conversations, summarizes interactions, and retrieves relevant knowledge instantly. In many organizations, it supports human agents by suggesting the next best action, generating customer responses, and handling multilingual or outbound communications. Platforms like Cyara’s conversational AI testing can ensure these AI systems perform reliably before they reach customers.
The results of integrating GenAI in CX have been impressive. IBM’s 2024 “Customer Service and the Generative AI Advantage” report found that:
- 65% of CX leaders expect GenAI to boost customer satisfaction.
- 46% anticipate a lower cost per contact.
- When integrating GenAI into customer service workflows, ROI jumps 37% for experienced AI users and 117% for newer adopters.
- Organizations experienced with conversational AI have seen cost per contact drop by as much as 25% when integrating GenAI.
Yet even as GenAI transforms customer service, it remains fundamentally reactive, depending on human prompts and single-task execution. What’s more, information about a customer’s history, billing, or prior interactions lives in separate applications that GenAI can’t always access or interpret. The result is strong conversation quality with limited end-to-end actionability.
Reaching that orchestration layer, where AI can coordinate across platforms and processes, is what defines the next leap.
What is agentic AI and how does it extend automation?
In a medium defined by human interaction—and its infinite possible directions—a “content on command” system can only go so far. Agentic AI takes a leap forward, introducing a system of independent software agents that can remember context, make decisions, and orchestrate workflows toward defined goals. Unlike other deterministic systems and applications, agentic AI doesn’t simply automates tasks, but reasons, learns, and interacts with customer in real time.
Agentic AI systems exhibit autonomy, goal-oriented reasoning, context persistence, and multi-agent orchestration. Where GenAI handles one conversation or task at a time, agentic AI operates across workflows and systems, bridging CRMs, ticketing tools, IVR, and knowledge bases to achieve outcomes.
Contact centers, by their very nature, are a powerful proving ground for this type of technology. They’re dynamic, data-rich environments where thousands of decisions unfold every hour, and CX quality depends on proactive support and fine-tuned customer journeys. Agentic AI can step in to detect service outages, alert customers, schedule technicians, and confirm resolutions, all without direct user intervention.
To put it another way, this is where AI shifts from response-driven guidance to results-driven automation.
Key differences: agentic AI vs. generative AI in contact centers
Here’s a side-by-side comparison to illustrate how this evolution from GenAI to agentic AI fundamentally changes the possibilities for CX.
|
Dimension |
Generative AI |
Agentic AI |
|---|---|---|
|
Initation |
Responds to user prompts or queries—e.g., “customer asks, system replies.” |
Acts autonomously based on defined goals, monitoring context, and triggering workflows without explicit human input. |
|
Scope of work |
Handles single, discrete tasks such as content generation, summarization, or drafting responses. |
Coordinates multi-step workflows, sequencing actions across systems and maintaining context over time. |
|
Behavior model |
Reactive—waits for events or requests to respond. |
Proactive—anticipates issues, initiates outreach, and drives tasks to resolution. |
|
Human role |
Human-in-the-loop: People interpret, validate, and act on system outputs. |
Human-on-the-loop: People oversee and manage exceptions while AI executes autonomously. |
|
System integration |
Operates at the surface level (chatbots, summaries, knowledge retrieval). |
Integrates deeply with CRMs, IVRs, ticketing tools, and other enterprise systems for orchestration. |
|
Focus |
Produces outputs—text, summaries, and responses. |
Achieves outcomes—resolves issues, completes workflows, and fulfills defined goals. |
|
Governance & risk |
Lower risk and simpler governance due to limited autonomy. |
Requires rigorous governance, guardrails, and explainability due to autonomous decision-making. |
|
Testing & assurance |
Can be validated through scripted, deterministic tests (known inputs and outputs). |
Requires continuous assurance tools to monitor unpredictable, non-deterministic interactions across infinite potential customer journeys (manual testing is no longer feasible.) |
How agentic AI transforms customer service operations
Analysts are bullish on the potential for agentic AI to radically reshape CX delivery. Gartner projects that by 2029, 80% of common customer service issues will be resolved autonomously, while Cisco estimates that 68% of interactions will be handled by agentic AI by 2028. Whatever forecast you follow, the theme is the same: agentic AI is poised to take over much of the customer interaction load.
At nearly every point in contact center CX, AI agents can have a measurable impact. For instance, they can:
- Assess customer context and predicted needs to dynamically route and triage interactions, reducing transfers and improving first-contact resolution.
- Continuously analyze conversations for quality and compliance, flagging anomalies and calling out coaching opportunities in real time.
- Generate and act on operational insights automatically, spotting emerging trends and performance gaps to trigger adjustments before they affect service.
Early deployments and analyst forecasts point to powerful results: shorter handle times, faster resolutions, higher containment rates, and improved agent productivity, all of which contribute to lower costs per interaction. Gartner predicts these efficiencies could reduce overall contact center operating costs by 30% by decade’s end. And CX gains are only the start—these improvements not only drive down costs but also increase revenue potential and strengthen compliance through continuous monitoring.
Equally important, yet perhaps easily overlooked, is how this shift redefines the role of human agents. Routine inquiries give way to higher-value interactions, where empathy is front and center in human-to-human service interactions. Meanwhile, new roles develop, such as AI overseers, agentic-AI trainers, and conversation designers. From the viewpoint of CX leadership, this AI transformation in customer service represents a move from reactive service delivery to proactive, orchestrated experiences.
Preparing for the agentic AI future in CX
Agentic AI is more than an upgrade to GenAI; it’s a rare technological leap that redefines how contact centers operate. Where GenAI responds, agentic AI anticipates. Where GenAI generates outputs, agentic AI drives outcomes. The shift from prompt-driven assistance to goal-oriented autonomy opens new possibilities for every aspect of customer service.
Yet, with opportunity comes significant responsibility. As AI agents take on more decisions and customer interactions, ensuring reliability, compliance, and performance becomes mission-critical. Traditional methods of assurance (and manual testing) can’t keep up with autonomous systems that learn and adapt in real time.
That’s where Cyara comes in. Our AI-assurance solutions help you test, monitor, and validate every interaction—so innovation never comes at the cost of customer trust.
Learn more about how Cyara is preparing for the future of agentic AI in CX.
