Beginner's Guide to Using Agentic AI in Python

Agentic AI is an exciting approach in which AI systems act as independent agents. These agents pursue goals, make decisions, and execute actions independently – often in a complex environment.

In this post, I'll show you how to implement a simple agent setup with Python in just a few steps – ideal for initial experiments with Agentic AI.

Requirements 🔧

Before you start, you should have the following installed:

• Python 3.8+

• openai (e.g., for GPT agents)

• langchain (framework for agent workflows)

• dotenv (for API keys)

Installation:

pip install openai langchain
python-dotenv

Simple agent setup with LangChain 🚀

from langchain.agents import initialize_agent,
load_tools
from langchain.llms import OpenAI
import os
from dotenv import load_dotenv

load_dotenv()

llm = OpenAI(temperature=0,
openai_api_key=os.getenv("OPENAI_API_KEY"))

tools = load_tools(["serpapi", "llm-math"],
llm=llm)

agent = initialize_agent (tools, llm, 
agent="zero-shot-react-description",
verbose=True
)

response = agent.run("What is the square
root of 144?")

print(response)

What's happening here? 🧠

• The agent uses GPT models from OpenAI.

• It can decide independently whether to research or calculate.

• Tools like `serpapi` (web search) or `llm-math` (math operations) help it with this.

Conclusion 💡

That was a very simple first introduction. In the next posts, I will show you

• How to integrate your own tools

• How to define goals for agents

• What a complete Agentic AI workflow looks like

#Python #AgenticAI #LangChain #Coding #AIDevelopment #Beginner