The AI revolution is in full swing. Anyone who has worked with ChatGPT already knows the power of generative AI: the ability of machines to create text, images, code or other content based on a simple prompt.
But technology doesn’t stand still. New AI systems go beyond content generation. They take initiative, plan actions and carry out complex tasks without constant supervision. This is what we call agentic AI.
But what exactly makes agentic AI different from the generative tools we’ve already become so familiar with? In this blog, we’ll explain the difference — and why it matters.
Back to basics: what does generative AI do?
Generative AI is designed to create something new based on existing data. Think of:
ChatGPT writing a blog post,
Midjourney generating an image,
GitHub Copilot completing your code.
How it works is relatively straightforward:
You provide a prompt → the AI generates an output → you decide what happens next.
The system is reactive and works step by step. You ask something, it responds. Want something different? Then you give it a new prompt.
Generative AI is therefore extremely powerful for:
Content creation (text, visuals, audio, video),
Ideation or inspiration,
Assistance with repetitive tasks.
But importantly: the user stays in control.
So how is agentic AI different?
Agentic AI is built to act autonomously. Instead of merely responding to prompts, it is given a goal — and then determines itself how to achieve that goal.
You no longer need to steer every step. The AI thinks, plans, executes and adjusts if necessary.
Concrete example:
Generative AI: You ask ChatGPT to write a marketing plan.
Agentic AI: You give the assignment “Start a campaign for our new product.” The AI then:
Requests relevant information (for example via integrations),
Analyzes the target audience and competition,
Creates a step-by-step plan,
Writes content, schedules posts, sends emails,
Tracks results and optimizes where needed.
Agentic AI thus combines multiple steps and technologies to independently achieve a result — with minimal user input.
Comparison: generative AI vs. agentic AI
| Feature | Generative AI | Agentic AI |
| Process | Prompt → output | Goal → plan → actions → result |
| Autonomy | Low | High |
| Steps | One at a time | Multiple in sequence, including feedback loops |
| User role | Directs every step | Defines goal, the rest runs autonomously |
| Strengths | Creative content, direct assistance | Autonomous task management, complex workflows |
Why the difference between these AI types matters
The difference between generative and agentic AI is not a nuance — it fundamentally changes what AI can do for you.
With generative AI, you work faster, more creatively and more productively. But you remain in the driver’s seat.
With agentic AI, you hand over part of the steering. You trust the AI to deliver the right result — much like delegating a task to a colleague or digital assistant.
For organizations, this means:
Time savings: Less micromanagement, less manual input.
Efficiency: Tasks that usually require multiple people or tools are centralized.
Scalability: One agent can manage multiple processes simultaneously.
Future readiness: Agentic AI lays the foundation for AI-driven operations.
Curious how your organization can respond to this evolution?
Where generative AI has helped us create faster, agentic AI promises to truly take work off our hands. The difference lies not only in what the technology can do, but also in how we collaborate with it as humans. Get in touch with our experts for a free AI inspiration session!
