Generative AI vs. Agentic AI
In the rapidly evolving world of AI, terms can sometimes get a little blurry. Let's clear up the crucial distinctions between Generative AI (Gen AI) and Agentic AI, which are often discussed interchangeably but have critical differences.
Generative AI: The Creator
• Core Function: Gen AI is all about "content creation". It can "create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request".
Think of ChatGPT drafting an email or Midjourney creating an image.
• Nature: It is fundamentally "reactive to the users input". You provide a prompt, it generates an output.
• Capabilities: Excellent for tasks like SEO content creation, marketing copy, product design, and customer support automation (e.g., chatbots answering common questions).
It analyses vast amounts of data to find patterns and trends.
Agentic AI: The Doer
• Core Function: Agentic AI "is focused on decisions as opposed to creating the actual new content". It describes AI systems "designed to autonomously make decisions and act, with the ability to pursue complex goals with limited supervision".
• Nature: It is "proactive". It doesn't solely rely on human prompts for every step, nor does it always require human oversight to perform a task. It can "adapt to different or changing situations and has 'agency' to make decisions based on context".
• Capabilities: It uses a four-step problem-solving approach: perceive, reason, act, and learn. This enables it to handle "multi-step reasoning" and "multi-step planning" to achieve complex, broader goals. Examples include autonomous vehicles, complex workflow management, and financial risk assessment.
The Relationship: Not Mutually Exclusive It's crucial to understand that Generative AI models are often "part of or a component of agentic AI systems". An LLM, which is a Generative AI, acts as the "brain" or "orchestrator" within an Agentic AI system, enabling it to reason, plan, and execute. Agentic AI is the "framework," and AI agents are the "building blocks" within that framework.
Some sources warn that the term "agentic AI" might just be hype, claiming it rebrands old, overly ambitious AI ideas without pointing to any real new progress. However, many others agree that agentic AI stands out because it focuses on AI systems that can act on their own and complete complex goals. They also say that building more autonomy in AI is a valuable goal, and researchers are doing important work toward that.
So, even if the term is sometimes debated, the core idea, AI being able to carry out multi-step tasks independently, is a real and meaningful development.