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 witho…
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.
Interview Points (1-Minute Pitch)
"Generative AI creates content reactively. Agentic AI achieves goals proactively."
- GenAI: Prompt → Output (ChatGPT drafts email).
- Agentic: Goal → Plan → Execute → Adapt (Hire engineer end-to-end).
- Built using: GenAI (LLMs) + RAG + Tools + Planning/Memory.