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Customer experience (CX) leaders are increasingly asking: What's the difference between generative vs agentic AI, and how does it impact the future of customer service? While both AI technologies leverage artificial intelligence and large language models (LLMs), they serve very different purposes. Understanding that difference is key to delivering intelligent, proactive customer experiences.
Learn other key concepts every CX leader needs to understand with The essential guide to AI customer service agents.
Generative AI vs agentic AI: What's the difference?
To understand the difference between generative AI and agentic AI, it helps to think of them as a talented writer versus a proactive project manager. While they often work together, they have fundamentally different "personalities" and goals.
The core difference between agentic AI vs generative AI
The biggest shift between the two is moving from content creation to goal execution.
Generative AI is reactive. It waits for you to give it a prompt and then uses patterns to "predict" the best response, whether that’s a paragraph of text, an image, or a snippet of code. It finishes its job the moment it provides the answer. It excels at creating content.
Agentic AI is proactive. Instead of just answering a question, it plans, problem-solves, and executes to achieve an objective. You give it a high-level goal (e.g., "Research this company and book a meeting with their sales head"), and it breaks that goal into steps, decides which tools to use, and executes them autonomously. It focuses on resolving problems and executing goals.
How agentic AI and generative AI work together
It isn't a case of "either-or." In fact, agentic AI uses generative AI as its "brain."
For example, imagine you want to plan a business trip:
The generative AI part: Writes the emails to your clients and summarizes the travel itineraries.
The agentic AI part: Checks your calendar for free slots, searches for flights within your budget, uses a credit card API to book the ticket, and adds the event to your calendar. If the flight the agent tried to book is suddenly sold out, the agentic system doesn't just stop and give you an error; it "reasons" that it needs to find a different flight and tries again. A standard generative chatbot would simply stop and wait for your next prompt.
Real-world use case: customer support
Generative AI: A chatbot that reads a customer's angry email and drafts a very polite, empathetic response for a human agent to review and send.
Agentic AI: A system that reads the email, identifies that a refund is needed, problem-solves to determine the correct resolution, checks the customer's purchase history, verifies the return policy, processes the refund in the payment system, and then emails the customer to let them know it's already done.
Key differences between generative vs agentic AI
Is agentic AI part of our GenerativeAgent® and CXP (customer experience platform)?
The answer: Yes.
GenerativeAgent is ASAPP’s generative AI agent that directly interacts with end customers. Because it’s generative, it goes beyond traditional rule-based automation in that it can reason and take action autonomously. These attributes also make it agentic - and it powers CXP, ASAPP’s agentic AI platform.
CXP is fully agentic in that it is a multi-agent system, where a variety of AI agents autonomously perform the tasks they are best at, including interacting with end customers, human agents, and each other to solve problems. With memory, observability, and safety and security that enterprises require.
