🧠 Introduction
Agentic AI is more than a buzzword—it's a new way of building intelligent systems that think and act on their own. While the concept may sound advanced, even beginners in data science can start experimenting with Agentic AI today.
This article will walk you through a simple project to create your first AI agent that automates a data science task. By the end, you'll understand how Agentic AI works in practice and gain a new skill for your portfolio.
🤔 What Will We Build?
We’ll build a "Daily Data Report Agent"—an AI assistant that:
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Pulls data from a public source (like a CSV link or API),
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Cleans and analyzes it,
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Generates a short summary,
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Emails or logs the report automatically.
This project shows how agents can complete multiple steps without needing manual input each time.
🧰 Tools and Libraries Needed
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Python 3.8+
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Pandas – for data handling
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LangChain – to build the agent workflow
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OpenAI API or GPT-4o – for summarizing results
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Schedule – for running it daily
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Optional: SMTP/Email API – to send email alerts
🛠️ Step-by-Step Guide
🔹 Step 1: Install Required Libraries
pip install pandas openai langchain schedule
🔹 Step 2: Load the Data (Example: COVID-19 Dataset)
import pandas as pd
url = "https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/latest/owid-covid-latest.csv"
df = pd.read_csv(url)
summary = df[['location', 'total_cases', 'new_cases', 'total_deaths']].sort_values(by='new_cases', ascending=False).head(5)
🔹 Step 3: Use OpenAI to Generate a Summary
import openai
openai.api_key = "YOUR_API_KEY"
prompt = f"Create a daily COVID-19 summary based on this data:\n{summary.to_string(index=False)}"
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
print(response['choices'][0]['message']['content'])
🔹 Step 4: Automate It with a Schedule
import schedule
import time
def job():
# Place all above code here
print("Report Generated")
schedule.every().day.at("09:00").do(job)
while True:
schedule.run_pending()
time.sleep(1)
🚀 Going Further: Make It a True Agent
To evolve this into a true Agentic AI system, you could:
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Add goal-setting (e.g., “If cases increase by 10%, trigger alert”)
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Include file storage or dashboard updates
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Connect to APIs (e.g., Slack, Email, Google Sheets)
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Chain multiple agents using LangChain
Tip: LangChain + GPT + a simple loop = your first real autonomous AI agent.
🎯 Why This Matters in 2025
Most automation today is reactive. But businesses need proactive systems that:
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Detect anomalies
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Make recommendations
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Send alerts or take actions—on their own
Agentic AI gives data scientists the tools to build such systems. And the best part? You don’t need a PhD—just curiosity and some Python.
🧾 Final Words
Agentic AI isn’t just for big tech companies. Anyone with basic Python and an interest in AI can start building intelligent, autonomous systems. Projects like these help you:
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Stand out in job interviews
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Boost your portfolio
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Prepare for the next wave of AI innovation
Start small. Think big. Let your agents do the work.