Agents vs Agentic in AI

Introduction

This guide explains the difference between AI agents and agentic AI. The goal is simple understanding. Next, you follow clear definitions, workflows, and examples.

هاد الشرح كيبين الفرق بين agent و agentic AI بطريقة واضحة. غادي تشوف التعريف، الاستعمال، و workflow ديال كل واحد.

Core Concepts Explained

An AI agent runs a short loop of sensing, processing, and acting. Agentic AI runs a long loop with planning, feedback, and correction. The second one focuses on goals, not single actions.

الـ agent كيخدم خطوة بسيطة. الـ agentic AI كيخدم process كامل فيه planning و feedback.

What Is an AI Agent

An AI agent is a program that takes input from an environment and returns an action. The pattern stays fixed and simple.

Sense the environment
Process the input
Return an action

AI agents handle single tasks. They follow instructions without extended reasoning.

Examples of Simple Agents

Chatbots with fixed rules
Recommendation engines
Basic automation scripts

What Is Agentic AI

Agentic AI goes beyond simple reactions. It understands goals. It builds plans. It breaks goals into tasks. It observes results and corrects the next step.

Agentic AI uses reasoning loops. It adapts when errors appear.

Key Features of Agentic AI

Goal driven behavior
Planning and task decomposition
Feedback loops
Autonomous execution
Self correction

Main Differences

Aspect	AI Agent	Agentic AI
Purpose	Single task	Goal with multiple tasks
Reasoning	Limited	Planning and reflection
Autonomy	Low	High
Adaptation	Low	Ongoing correction
Workflow	One loop	Multi step chain

Simple Flow of Each System

Agent Flow

Input
Process
Action

Agentic AI Flow

Understand goal
Plan tasks
Execute steps
Review output
Fix errors
Reach result

Where They Are Used

AI Agents

Customer support bots
Email sorting tools
Simple automation systems

Agentic AI

Research assistants
Code generation workflows
Business automation chains
Multi step data processing

Agents vs Agentic in Moroccan Darija

ف AI، كاين فرق كبير بين agent و agentic system. الـ agent كيخدم step wahda. الـ agentic AI كيخدم workflow كامل فيه planning و feedback و correction.

Shno Howa Agent

Kaydkhl input
Kay7seb
Kaydir action wahda

Shno Howa Agentic AI

Kayfhem l goal
Kaykhtat tasks
Kaydir steps m3a feedback
Kayss7 l errors

Far9 Sarih

Agent simple
Agentic kayplanning
Agent kay7der step wahda
Agentic kay7der workflow kamel

Syntax or Model Structure Example

Below is a small Python example showing a very simple agent vs a simple agentic loop.

# Simple agent
def agent(input_data):
    if input_data == "spam":
        return "Filter"
    return "Allow"

print(agent("spam"))

# Simple agentic loop
def agentic(goal):
    steps = ["plan", "execute", "review", "fix"]
    log = []
    for step in steps:
        log.append(f"{step} for {goal}")
    return log

print(agentic("analyze report"))

مثال بسيط يبين الفرق بين agent و agentic workflow.

Exercises

Write a one sentence definition of an AI agent.
List three features of agentic AI.
Explain why an agentic system needs feedback loops.
Create a three step plan for an agentic system that organizes files.
Write a Python function that simulates a simple agent.
Modify the example code to add a new step in the agentic loop.
List cases where agents work well.
List cases where agentic AI works better.
Describe one risk of using agentic AI in complex workflows.
Design a small agentic workflow for data cleaning.

Conclusion

AI agents react. Agentic AI plans. The difference shapes modern AI systems. Working with both helps you understand classic and modern AI behavior.

ال agent كيخدم responses بسيطة. الـ agentic AI كيخطط و كيصلح. وهنا كيبان التطور الكبير ف AI.

Ai With Darija