Agentic AI vs. generative AI

For businesses, the distinction between agentic AI and generative AI is less about what’s under the hood and more about how each contributes to getting work done. Generative AI produces content and context when prompted. Agentic AI takes on a broader role, using that output to guide decisions and complete tasks. Learning a bit more about how they differ helps teams apply these complementary technologies in increasingly powerful ways.

What is the difference between agentic AI and generative AI? 

Both agentic and generative AI (GenAI) share the capacity to evolve and learn from data and experience. And while they are often used together, they serve different purposes and have a few key distinctions:

• Generative AI explained

Generative AI takes inputs (or prompts) and generates something new, such as text, images, or even a block of code. GenAI models are trained on massive datasets and learn patterns that let them respond in often surprisingly relevant ways. They’re great at things like summarizing reports, writing blocks of copy, or creating visual layouts.

• AI agents explained

AI agents typically use generative AI, but they take things a step further. Instead of having to wait for a prompt, they can autonomously decide what to do next based upon established goals. They observe what’s happening, plan a response, take action, and learn from the outcome – all in a loop. This makes them useful for automating processes, coordinating systems, or solving problems without constant human input.

• Agentic AI explained

Agentic AI refers to the framework achieved when multiple AI agents are integrated and coordinated within a single system or platform, operating toward an established goal. Agentic AI connects tools, data, reasoning, and action. And does so not just within one task, but across many. An agentic AI framework lets you guide agents, call on generative tools, trigger workflows, and keep things aligned to a broader objective.

So, in a nutshell: GenAI generates and agentic AI acts. GenAI can take all your research and meeting notes from a product launch planning session, for example. It can summarize them, organize the information, and suggest possible next steps. Whereas agentic AI can take those steps, assign owners, update systems, follow up on progress, and adjust the plan as conditions change.

Compare generative AI vs. agentic AI 

How are GenAI and agentic AI typically used? 

Not every task needs decision-making. And not every task needs content creation. Most modern businesses that use AI agents also use GenAI. And even when they operate inside the same system, each has its own operational wheelhouse. Here is a handful of examples that illustrate the typical uses for each technology:

What generative AI is typically used for:

• Writing internal documents, meeting summaries, or training materials
• Drafting customer-facing content like responses, messages, or updates
• Creating visual mockups, branded assets, or product images/specs
• Rewriting or translating content into different styles, tones, or formats
• Answering questions based on documents, reports, or knowledge bases
• Suggesting ideas or exploring options in early-stage planning

What agentic AI is typically used for:

• Noticing when action is needed and responding without a prompt
• Following a multi-step process from beginning to end
• Using internal tools to execute decisions, like updating a record or triggering a workflow
• Prioritizing tasks based on changing inputs or deadlines
• Monitoring progress and adjusting future actions based on results
• Coordinating across tools or systems to reach a goal