Agentic vs. generative AI - what's the difference?

Two terms that are frequently used in the context of AI are "agentic AI" and "generative AI". While both technologies fall under the broad umbrella of AI, they are fundamentally different in how they work and their applications. It is important to understand these differences in order to fully realize the impact and possibilities of these technologies.

Generative AI - The Creative

Generative AI is the creative force of artificial intelligence. It was developed to create new content - be it text, images, music, code or videos. The basic principle of this technology is to learn from existing data and use it to generate new, original content that is modeled on human creativity. Examples include programs such as ChatGPT, DALL-E or MidJourney, which are now being used in many industries to revolutionize the marketing and entertainment industry.

However, generative AI has its limits. It produces content based on learned patterns without really "understanding" the work created. It can therefore produce biased or incorrect output if the training data is incomplete or distorted. This type of AI also works reactively. It does not make its own decisions or carry out actions.

Agentic AI - The autonomous

In contrast, agentic AI not only focuses on creating content, but also acts autonomously to achieve specific goals. These systems are capable of making decisions, learning from previous experience and adapting to changing environments. Examples of applications include autonomous vehicles or systems for automatic process control. Here, agentic AI takes responsibility for decisions that require direct action. Basically, you can say that an AI is agentic if it is able to use tools independently to carry out tasks.

However, the basis for both types is the same....