CHART 1: THE GenAI MATURITY MODEL
Agentic AI combines the power of generative AI( GenAI), large language models( LLMs), real-time data, and autonomous decision-making to act with intent. It doesn’ t just answer questions; it completes objectives.
Unlike traditional automation, which follows predefined instructions, agentic AI can interpret context, make data-driven decisions in real time, and coordinate across systems to drive outcomes: not just complete tasks.
In comparison to traditional rep virtual assistants and customer chatbots, which focus on supporting human users in specific interactions, agentic AI autonomously orchestrates multi-step, cross-system actions. This achieves business goals without constant human prompting.
Think of it as a shift from task execution to goal fulfillment. CHART 1 shows the evolution of self-service and the maturity of the GenAI model.
Agentic AI doesn ' t just answer, say a billing question; it proactively adjusts the plan, updates back-end systems, and confirms resolution with the customer. It acts like a digital co-worker: autonomous yet collaborative, informed yet adaptive.
This evolution is not theoretical; it’ s already underway. Leading enterprises are deploying agentic systems that detect churn risk, resolve issues before customers complain, and even recommend products based on behavioral signals.
As customers demand faster, more personalized service, agentic AI is emerging not just as a tool but as the engine of the modern contact center.
Rather than waiting for customers to reach out, agentic AI can preempt issues, proactively intervene, and autonomously close the loop: often before the customer even knows there ' s a problem. According to Gartner, by 2029, agentic AI will autonomously resolve 80 % of common customer service issues without human intervention.
As contact centers mature from task automation to goal-driven AI orchestration, agentic systems become essential to scale quality service.
CHART 1: THE GenAI MATURITY MODEL
Later on in this article there is a sideby-side comparison that clarifies how agentic AI goes far beyond traditional automation( see TABLE 1).
REAL-TIME AUTONOMY IN PRACTICE
Agentic AI is already proving its value across industries.
• Telecoms are using predictive AI to alert customers of outages before they report them: shifting from reactive troubleshooting to proactive reassurance.
• Banks are piloting autonomous agents that detect fraud risks or suggest personalized financial products, based on behavioral signals and transaction patterns.
• Contact centers are deploying GenAI assistants that surface real-time insights to human agents, reducing average handle time( AHT) and improving first contact resolution( FCR).
These examples showcase a shift from AI as a support tool to AI as the primary driver of engagement, capable of navigating complexity, integrating data, and optimizing service delivery.
WHY REAL-TIME DATA IS MISSION-CRITICAL
Implementing agentic AI isn’ t just a technology upgrade; it’ s a fundamental operational shift. And at the heart of this transformation is data.
Agentic AI can’ t function without visibility. Without access to real-time, contextualized customer data, even the
AGENTIC AI
SOURCE: K2VIEW
most advanced AI agents are effectively blind; they are unable to understand what’ s happening, what the customer needs, or what action( s) to take.
To act with autonomy and intelligence, agentic AI systems must have immediate access to clean, unified, and live data across systems, channels, and touchpoints.
This level of responsiveness demands more than traditional data warehousing or periodic syncs. It requires an infrastructure built for data-in-motion: capable of delivering personalized insight in milliseconds and supporting thousands of concurrent decision processes.
ADDITIONAL( AND HUMAN) CAPABILITIES NEEDED
Beyond the data layer, successful adoption of agentic AI requires additional organizational and operational capabilities.
• Human – AI Collaboration. As AI takes on high-volume tasks, human agents evolve into escalation experts, empathy specialists, and experience designers. New roles are emerging, like AI trainers and journey orchestrators; people who ensure agents and AI work in harmony to deliver seamless outcomes.
• Governance and Control. Agentic systems must operate within carefully defined guardrails. Clear boundaries on autonomy— such as requiring human approval for refunds or account closures— ensure safety and compliance. Strong data governance is essential, including privacy, traceability, and auditability of AI decisions.
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