Agentic AI vs Generative AI: Understanding the Key Differences and Impacts
Large Language Models (LLMs) (opens new window) like GPT (opens new window) can generate text, answer questions, and assist with many tasks. However, they are reactive, meaning they only respond to the input they receive based on patterns they've learned. LLMs can't make their own decisions; other than that, they can't plan or adapt to changing situations.
Agentic AI (opens new window) comes into play to resolve this issue. Unlike **generative AI (opens new window) ** LLMs, agentic AI can take initiative, set goals, and learn from its experiences. It is proactive, able to adjust its actions over time, and can handle more complex tasks that require ongoing problem-solving and decision-making. This shift from reactive to proactive AI opens up new possibilities for technology across many industries.
In this blog series, we will break down the differences between Agentic AI and Generative AI, looking at how each affects industries and the future of technology. In this first post, we'll start by exploring what sets these two types of AI apart.
