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. Here’s how both technologies can empower the customer service experience, today and in the near future:
- Supercharging customer support automation – Gen AI is enhancing efficiency by automatically generating responses to customer service inquiries, crafting answers to common questions and troubleshooting in real-time. For example, an e-commerce business can deploy Gen AI-powered chatbots to manage order status updates, refund requests and shipping questions – delivering quick, accurate support without human intervention.
- Revolutionising contextual and autonomous service – Traditional customer service chatbots often struggled with complex queries due to their rule-based design, frequently escalating cases to human agents. Agentic AI turns that on its head. It can interpret customer intent and emotion, take meaningful action and resolve issues without human input. Going beyond reactive support, agentic AI predictively assesses situations to deliver smoother, more personalised interactions.
- Empowering human teams to deliver exceptional service – Agentic AI can also take on tedious behind-the-scenes chores such as gathering, cleaning and formatting data. Automating these time-consuming tasks frees up human teams to focus on high-impact work that drives loyalty and deepens customer relationships.
Navigating the future of AI in customer experience
As AI continues to evolve, understanding the distinctions between generative and agentic AI is intrinsic to unlocking the full potential of these innovative tools – especially when it comes to automating scalable, personalised communications that add value for your customers. And while Gen AI is already reshaping content creation, agentic AI is set to usher in a new dawn of autonomous, goal-driven experiences.
The key to success is knowing how and when to apply each. Businesses that get this right can deliver personalised interactions at scale, while freeing up human teams to focus on what really matters.
Confused about all things AI? Whether you’re exploring your first chatbot or considering how agentic AI could fit into your journey orchestration strategy, we’re here to help. Get in touch to learn how we can add value to your AI roadmap.
