Agentic AI vs. Generative AI: What CX Leaders Need to Know

✍️ by Rachel Ryan , Product Manager at Engage Hub

AI has dominated technology-related conversations for over a decade – but more recently, generative AI and agentic AI have been competing for centre stage. While traditional AI excels at pattern recognition and data analysis, generative AI (Gen AI) has reshaped the playing field with its ability to create new content – with tools like ChatGPT clearly demonstrating its power.

However, as AI capabilities evolve, the new kid on the block – agentic AI – is beginning to steal the limelight. Unlike Gen AI, which relies on human prompts to generate outputs, agentic AI is all about decision-making – think autonomous vehicles and virtual assistants.

Understanding the distinction between Gen AI and Agentic AI is critical when it comes to delivering meaningful customer experiences. In this article, I explore key differences and explain how each technology works to drive innovation, increase personalisation and improve decision-making, at scale.

🚀 Understanding the differences between agentic AI and Gen AI

Both Gen AI and agentic AI represent major advancements in AI. However, they serve different purposes – and distinguishing between them is key to unlocking next-generation customer experiences.

• Gen AI is a creative, reactive and increasingly high profile, thanks to accessible tools like ChatGPT and Deepseek. It excels at mimicking human output when producing text, images, software code, audio and more – typically in response to human prompts. It does this using deep learning models – algorithms that simulate the learning processes of the human brain – and other technologies like robotic process automation (RPA). These models have the capability to identify and encode patterns and relationships in huge amounts of data – using it to understand natural language and generate high-quality content.
• Agentic AI can make decisions. Instead of waiting for instructions, agentic AI systems act autonomously, understanding a customer’s goal and orchestrating a series of steps to achieve it. As such, it’s transformational in applications like robotics and complex analysis. Uniting flexible large language models (LLMs) with accurate traditional programming, machine learning (ML), and natural language processing (NLP) within a digital ecosystem, agentic AI takes action – and often independently.

In simple terms, Gen AI is a reactive content creator – while agentic AI is a proactive outcome achiever.

🚀 Agentic AI vs. AI agents: The framework and its building blocks

To distinguish between agentic AI and AI agents, think of the former as the framework and the latter as building blocks that operate within it.

The overarching system – agentic AI – is designed to solve problems and achieve goals with minimal human oversight. It interprets user intent, contextual data and desired outcomes, then dynamically orchestrates tasks, getting the job done.

Meanwhile, AI agents are smaller, task-oriented components within that framework. Each agent handles a specific task or process with a degree of autonomy. They’re akin to intelligent workers – each focused on their own assignment but aligned to a shared goal.

A useful analogy is a smart home. Agentic AI oversees the energy management strategy, responding to user preferences and real-time data. It delegates to AI agents like thermostats, lights or smart appliances – each with its own function, yet all contributing to the homeowner’s energy efficiency goal.

This model is changing how humans interact with AI, from issuing one-off prompts to setting goals and letting intelligent systems manage the details.

🚀 Agentic AI and Gen AI applications in customer service

While Gen AI already has a strong foothold in customer service, many applications of agentic AI are still emerging. But the potential is exciting.. (..)

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