Agentic AI

Agentic AI is a category of AI systems, also called AI agents or autonomous agents, that can independently plan, decide, and act on behalf of users to achieve complex goals, with minimal human input. 

Unlike traditional AI systems that respond to single-step prompts, Agentic AI takes initiative. It can decompose tasks, reason through multi-step workflows, and monitor outcomes to adjust its approach dynamically.

How does agentic AI work?

Agentic AI systems operate in a cycle of perception, reasoning, action, and learning. This approach allows them to handle complex tasks more independently and improve over time. Here's how each step works:

• Perceive: AI agents take in information from different sources—like sensors, databases, or apps—and make sense of it by recognizing patterns, identifying key details, or spotting important objects.
• Reason: A language model serves as the brain of the system, figuring out what needs to be done and how to do it. It pulls in data, makes decisions, and can call on other tools—like generators or search systems—to get the right answers.
• Act: After a plan is created, the AI can execute it by interfacing with other tools or software via APIs. Built-in rules may restrict its actions. For instance, it may be allowed to handle only specific tasks while routing more complex ones to human operators.
• Learn: AI agents get better over time by learning from what happens during their interactions. This feedback loop helps improve accuracy, speed, and overall performance, making the system more useful the more it's used.

This cycle makes agentic AI powerful for real-world use like automating tasks, supporting decision-making, and adapting over time. It's not just about reacting to commands, but learning and improving with every interaction.

Agentic AI transforms customer experience (CX)

Agentic AI is transforming customer experience by managing full-service tasks like verifying identity, placing orders, and sending confirmation emails without human help. These systems improve key metrics, including higher customer satisfaction scores (CSAT) and increased Net Promoter Scores (NPS), by delivering efficient support. 

Agentic AI also connects across tools like CRMs, payment systems, and inventory, creating smoother, more personalized experiences. When agentic AI handles repetitive tasks, it frees human agents to focus on complex cases and can improve productivity and morale. As this level of smart, seamless service becomes the standard, customers will expect nothing less.

Challenges & considerations

While Agentic AI offers powerful benefits, it also introduces new challenges that businesses must carefully manage, including: 

• Guardrails and oversight: Autonomous agents require well-defined boundaries to prevent actions outside policy or legal frameworks.
• Accountability and ethics: As agents make decisions, questions arise on liability, bias, and transparency.
• Integration complexity: Connecting agents to varied systems—CRMs, inventories, payment gateways—demands mature architecture and secure APIs.
• Data quality: Poor or incomplete data undermines decision-making. High-quality, well-governed data is vital.

Agentic AI is the next step in an AI-first future: autonomous systems that plan, act, learn, and manage full customer journeys. In CX, they turn simple interactions into seamless, end-to-end experiences like booking, fulfillment, and support. As AI moves from reactive to proactive, Agentic AI is becoming essential to the future of CX.