Welcome to Day 1 of this 7-day beginner-friendly series where we’ll build a fully working Agentic AI Task Helper using free tools, powered by Google AI Studio (Gemini Free Tier).
By the end of today, you’ll understand:
- What agentic AI really is
- How agents differ from regular chatbots
- What we will build over the next 7 days
- How to create your project structure and run your first stub
Let’s begin.
What’s the Difference Between a Chatbot and an Agent?
Most people are familiar with AI as something that simply responds to questions — like a typical chatbot.
A chatbot:
- waits for input
- generates one response
- stops
If you’re new to AI and want a simple introduction to modern tools, you may find my guide helpful:
👉 ChatGPT for Beginners: 7 Easy Ways to Boost Productivity with AI
But agentic AI goes beyond this.
What Is an Agent?
An agent doesn’t just answer — it acts with intention.
It follows a mini “thinking loop”:
goal → plan → reason → act
Meaning:
- Goal: The user gives an objective
- Plan: The agent breaks it down
- Reason: It decides how to approach it
- Act: It produces structured output (or even takes actions)
If you want a deeper explanation of the agentic shift happening in AI, here’s a great intro from your own blog:
Beginner’s Guide to AI Agents: Smarter, Faster, More Useful
This is exactly the type of system we’re building in this series.
What We’re Building in This Series
Our project is called:
Task Helper Agent (Gemini Edition)
A simple agent that takes a user goal like:
“Learn Python in 2 weeks”
And turns it into a structured plan:
- Learn syntax basics
- Practice exercises
- Build small projects
- Review concepts
- And more…
This agent will grow each day:
- Day 1: Structure
- Day 2: Connect Gemini
- Day 3: Agent planning
- Day 4: Agent reasoning
- Day 5: CLI
- Day 6: Memory
- Day 7: Custom agent
Tools You’ll Use (All Free)
1. Python 3.9+
Lightweight and perfect for agent workflows.
2. Google AI Studio (Gemini Free Tier)
We’ll generate text and reasoning with the Gemini API.
Your readers may appreciate this guide for context:
Google Gemini Made Easy: A Beginner’s Guide to AI-Powered Answers
3. google-genai
Official Python library for Gemini.
4. Command Line Interface (CLI)
We’ll run the agent right from your terminal.
Project Structure Setup
Create this folder:
ai-task-helper/
Inside it, add:
ai-task-helper/
├─ src/
│ ├─ main.py
│ ├─ agent.py
│ ├─ llm_client.py
│ ├─ config.py
│ └─ memory.py (empty for now)
├─ requirements.txt
├─ README.md
├─ .gitignore
What Each File Does
- main.py — Runs your agent
- agent.py — Contains planning & reasoning logic
- llm_client.py — Connects to Gemini (Day 2)
- config.py — Stores model names/settings
- memory.py — Will store past plans (Day 6)
- requirements.txt — Dependencies
- README.md — Project overview
Add the Initial Stub Code
src/main.py
from agent import TaskHelperAgent
def main():
agent = TaskHelperAgent()
print("AI Task Helper Agent (stub)")
print(agent.plan_task("example: learn Python in 2 weeks"))
if __name__ == "__main__":
main()
src/agent.py
class TaskHelperAgent:
def __init__(self):
pass
def plan_task(self, user_goal: str) -> str:
return f"[STUB] Plan for: {user_goal}"
src/llm_client.py
def generate_text(prompt: str) -> str:
raise NotImplementedError("Gemini client not set up yet.")
src/config.py
MODEL_NAME = "gemini-2.5-flash"
requirements.txt
google-genai>=0.1.0
Run Your First Stub
Inside your project folder, run:
python src/main.py
You should see:
AI Task Helper Agent (stub)
[STUB] Plan for: example: learn Python in 2 weeks
Perfect — your structure is ready for Day 2!
Coming Up in Day 2
Tomorrow, you’ll:
- Generate your Google AI Studio API Key
- Connect your code to Gemini Free Tier
- Make your first real AI response
- Replace the stub with real intelligence
Your agent will officially take its first breath tomorrow.
