Researchers at IBM Research and OpenAI analyze the paradigms of generative AI versus agentic AI, detailing transformer, GAN, VAE, and reinforcement-learning architectures. They examine content-creation capabilities versus autonomous multi-step decision-making and highlight key use cases and limitations.

Key points

• Transformer-based generative models (e.g., GPT, diffusion) use attention mechanisms to synthesize text and images by learning data distributions.
• Agentic AI combines LLMs, planning algorithms, reinforcement learning, and tool-use frameworks to autonomously execute multi-step objectives and adapt to dynamic environments.
• Both paradigms face technical challenges: generative AI hallucinations and data biases; agentic AI alignment issues, governance complexity, and high compute demands.

Why it matters: Distinguishing generative from agentic AI guides strategic adoption, enabling organizations to leverage both creative content generation and autonomous decision-making while mitigating risks like hallucinations and misalignment.