Generative vs Agentic AI Explained

In the rapidly evolving realm of artificial intelligence, the distinction between generative and agentic AI is becoming increasingly relevant. The essence of generative AI lies in its reactive nature, responding to prompts with content generation capabilities that extend from text to images and audio. This form of AI, as discussed by Martin Keen in the IBM Technology video “Generative vs Agentic AI: Shaping the Future of AI Collaboration” published on April 21, 2025, functions much like a sophisticated pattern-matching machine, relying on learned statistical relationships from vast datasets. The core of its functionality ends upon generation, awaiting further human interaction.

However, Keen delves into the capabilities of agentic AI, contrasting it with its generative counterpart. Unlike generative AI, agentic systems are proactive, designed to perceive environments, make decisions, and execute actions while learning from outcomes. This cycle requires minimal human intervention and is driven by large language models (LLMs), which Keen highlights as essential for both generative content and agentic reasoning capabilities.

Keen provides practical instances, such as using generative AI for creative content creation, illustrating its usefulness in helping creators improve efficiency. He exemplifies this by sharing his personal experience of employing generative AI to assist in writing fan fiction. While generative AI aids in idea generation, human interaction remains crucial to curating and refining these outputs, as demonstrated in the context of YouTube content creation.

Agentic AIs, on the other hand, excel in dynamic, multi-step tasks such as personal shopping or conference planning, leveraging LLMs for chain of thought reasoning. This method breaks down complex tasks into manageable steps, much like human problem-solving, effectively turning AI into a proactive collaborator.

Furthermore, Keen forecasts a future wherein the most efficient AI systems merge generative and agentic qualities. This hybrid model could lead to intelligent AI collaborators capable of balancing content creation and decisive actions, tailoring their approach to the task at hand. The conversation ultimately leaves us contemplating the possibilities of AI-driven evolution in technology and collaboration—ripe with potential yet demanding careful integration to maximize its benefits while addressing inherent challenges.