Generative vs Agentic AI Shaping the Future of AI Collaboration
AI Summary
Summary: Difference Between Generative AI and Agentic AI
- Generative AI
- Definition: Reactive systems that generate content based on user prompts.
- Functionality: Generates text, images, code, or audio using patterns learned during training on massive datasets.
- Nature: Sophisticated pattern-matching; operations end at generation without further steps.
- Agentic AI
- Definition: Proactive systems that take action towards goals following a user prompt.
- Functionality: Perceives environment, decides and executes actions, learns from outputs with minimal human intervention.
- Common Foundation:
- Both approaches often utilize Large Language Models (LLMs) as their backbone.
- LLMs support both generative tasks and provide reasoning for agentic actions.
- Real-World Applications:
- Generative AI in creative tasks, e.g., content creation and reviewing scripts.
- Agentic AI in scenarios requiring multi-step processes, e.g., personal shopping agents managing purchases, monitoring prices, and coordinating deliveries.
- Reasoning Capabilities:
- Agentic AI employs LLMs for chain of thought reasoning, breaking down complex tasks into actionable steps.
- Example: Organizing a conference involves a logical process of determining requirements, researching venues, and checking availability.
- Future of AI:
- The most powerful AI systems will likely combine both generative and agentic capabilities, acting as intelligent collaborators.
- Example: An agent that generates contextual content for projects or tasks as needed.