What Is The Difference Between Agentic AI And Generative AI

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Generative AI vs Agentic AI

The Complete 2025–2026 Beginner-Friendly Guide

If you’ve been hearing the words “Generative AI” and “Agentic AI” everywhere in 2025–2026 but still feel confused, you’re not alone.

Most articles make it sound complicated. This guide explains everything in super simple words even a 15-year-old can understand — while giving you the expert-level depth Google loves for top rankings.

By the end, you’ll know:

What each one actually does
The 9 biggest differences (table included)
Real-life examples of both (ChatGPT, Claude, Grok, AutoGPT, Devin, CrewAI, etc.)
Which one will create more jobs and money in 2026–2030
Future predictions from OpenAI, Anthropic, Google DeepMind & xAI

Let’s start with the basics.

What is Generative AI?

Generative AI = the AI that creates things. You give it a prompt → it outputs text, image, video, music, or code. It stops when the content is finished. Examples: ChatGPT, Claude, Midjourney, Llama 3, Grok, Stable Diffusion.

Now let’s go much deeper into the real star of 2026…

Example of Generative AI in action: You type: “Write a funny LinkedIn post about quitting my job to become a full-time cat influencer.” → ChatGPT instantly writes a perfect post for you. That’s generative AI — it generates content, but it stops there.

How To make Simple Chat bot With Generative AI

Install Required Packages

pip install langchain-groq langchain

Import Required Libraries
Add Your API Key
Create the LLM Client
Send a User Question
Get Response From the Model

from langchain_groq import ChatGroq

groq_api_key = "YOUR_API_KEY_HERE"

llm = ChatGroq(
    model="llama-3.1-8b-instant",
    api_key=groq_api_key
)

user_input = "What is AI?"


response = llm.invoke(user_input)
print("Bot:", response.content)

What is Agentic AI?

Agentic AI is the next evolution of artificial intelligence. In simple words: Agentic AI is an AI that can act like a human employee — it sets goals, makes plans, uses tools, takes actions, learns from results, fixes mistakes, and keeps working until the entire job is 100% done — all with little or no human help.

It can:

Think step-by-step
Use tools (Google search, code editor, email, calendar, etc.)
Remember long-term goals
Fix its own mistakes
Keep working until the job is done

Real Agentic AI example: You tell Devin: “Build me a full Netflix clone with user login, payment, and video streaming.” → Devin opens VS Code, writes code, fixes bugs, deploys to Vercel, and gives you the live link — all without you touching anything. That’s agentic AI — it acts like a human worker.

Simple Langgraph AI Agent

This code creates a simple AI assistant using LangGraph. The assistant can chat with the user and intelligently call tools whenever needed. It includes only two tools:

1️⃣ Add Numbers Tool

A small function that takes two numbers and returns their sum.
Example: “Add 5 and 7” → AI will call the tool and return 12.

2️⃣ Tavily Web Search Tool

A search tool powered by Tavily that can fetch real-time information from the web.
Example: “Search latest news about AI” → AI uses Tavily and summarizes the results.

🔧 How the Bot Works

The LLM (Gemini 2.0 Flash) handles conversation.
If the user asks something requiring a tool, LangGraph automatically:
- Detects the need
- Calls the tool
- Sends the result back to the assistant

Imports All

import os
from dotenv import load_dotenv
load_dotenv()
from langchain_google_genai import ChatGoogleGenerativeAI
from tavily import TavilyClient
from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_core.tools import tool
from langgraph.graph import StateGraph, START, END
from langgraph.graph.state import MessagesState
from langgraph.prebuilt import ToolNode, tools_condition

from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

LLM Client

llm = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash",
    api_key=os.getenv("GOOGLE_API_KEY")
)

Tools Setup

# 1) Simple Addition Tool
@tool
def add_numbers(x: int, y: int) -> int:
    """Return the sum of x and y."""
    return x + y


# 2) Tavily Web Search Tool (max 2 results)
web_search = TavilySearchResults(max_results=2)

Binding Tools with LLM

tools = [add_numbers, web_search]

llm_with_tools = llm.bind_tools(tools)

Assistant Function

def assistant(state: MessagesState):
    messages = state["messages"]

    # If conversation is empty → greet
    if len(messages) == 0:
        return {"messages": [AIMessage(content="Hello! How can I help you today?")]}

    # Otherwise pass messages to LLM (with tools)
    return {"messages": [llm_with_tools.invoke(messages)]}

Buiding LangGarph Nodes & Edges

# Build LangGraph
# ---------------------------
builder = StateGraph(MessagesState)

builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))

builder.add_edge(START, "assistant")
builder.add_conditional_edges("assistant", tools_condition)
builder.add_edge("tools", "assistant")

graph = builder.compile()

Run Graph

def run_bot(user_message: str):
    initial_input = { "messages": [HumanMessage(content=user_message)] }

    for event in graph.stream(initial_input, {}, stream_mode="values"):
        if event.get("messages"):
            event["messages"][-1].pretty_print()

Agentic AI Is not all about Coding - You can Learn and Build Everything Without Learning Code -

That what N8N

A Low - Code AI Automation Platform

Deep Dive: No-Code Agentic AI in 2025–2026 (Why n8n Is Winning)

You don’t need to code to build powerful agents anymore.

The leader in no-code/low-code agentic AI right now is n8n (pronounced “nate-n”).

Why everyone is switching to n8n in 2025–2026:

100% open-source & self-hostable (your data stays yours)
400+ native app integrations (Google, Slack, Airtable, Stripe, etc.)
Built-in LangChain nodes → drag-and-drop ReAct agents, tool calling, memory, RAG
Human-in-the-loop approvals, error handling, logging, replay
Used by Fortune 500 companies for production agents (not just prototypes)
Free forever if you self-host

Real examples people run on n8n today: → Auto-research + write + publish blog posts → Lead generation machine (LinkedIn → enrichment → email) → Customer support agent that answers tickets 24/7 → Full invoice processing pipeline

Start here → https://n8n.io (free templates included).

Just add these three sections to your current blog — no need to rewrite anything else.

If you Want to Learn More About AI We Have A detail RoadMap To Become AI Developer

We Hope This Was Help Full

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