Generative AI vs Agentic AI

#ai #machinelearning #agents #automation

Generative AI vs Agentic AI: Capability vs Behavior

Generative AI and Agentic AI are often discussed together, but they represent two very different layers of intelligence.

Generative AI is primarily about creation.

It reacts to a prompt and generates content-text, images, code, or audio-based on learned patterns. Its role is largely responsive: it waits for instructions and delivers an output. In this sense, Generative AI acts as a powerful capability, not an independent decision-maker.

Agentic AI, on the other hand, is about achieving goals.

Instead of simply responding to a single prompt, Agentic AI can plan, reason, take actions, evaluate outcomes, and iterate until a goal is accomplished. It is proactive and autonomous, capable of deciding what to do next without continuous human intervention.

Think of it this way:
Generative AI answers questions
Agentic AI solves problems

Importantly, Generative AI is a building block of Agentic AI. Agentic systems often rely on generative models for reasoning, communication, and content generation-but they go beyond generation by adding decision-making, memory, and control loops.

In short:
Generative AI = Capability (what the system can do)
Agentic AI = Behavior (how the system acts to achieve goals)

As AI systems evolve, the shift from purely generative tools to agentic systems marks a move from assistance to autonomy-and that distinction will define the next generation of intelligent applications.

What is Generative AI?

Generative AI focuses on creating human-like content such as text, images, code, or videos-only when prompted.

Examples:
ChatGPT → text & code
DALL·E → images
Sora → video
Nature: Reactive
You ask a question → it generates a response
No initiative, no long-term goal tracking

Generative AI is incredibly powerful, but it operates in a request-response loop. It does not decide what needs to be done next.

What is Agentic AI?

Agentic AI is designed to achieve goals autonomously.
Instead of waiting for prompts, it:
Understands a high-level objective
Breaks it into steps
Uses tools and data
Executes actions
Monitors results and adapts
Nature: Proactive
You give a goal → it plans, executes, and iterates
Can seek approvals, retry failed steps, and optimize outcomes

In short, Agentic AI behaves more like a digital worker than a smart assistant.

Evolution Through Hiring Example

The video shows 4 stages improving a chatbot for hiring a backend engineer:

Basic GenAI: Helps draft JD, emails, questions → You do all actions manually.
RAG (Retrieval-Augmented): Uses company docs → Tailored JDs, questions.
Tools Added: APIs for LinkedIn, email, calendar → Auto-posts jobs, schedules.
Agentic AI: Understands goal → Plans entire flow, monitors applications, adapts (boosts job if low apps), handles onboarding.