Build AI Agent Tool Calling with OpenAI + Python 🤖 | Automatic Tool Calling | Agentic AI Tamil

How can an AI Agent automatically decide which tool to use based on a user's request?

In this practical Tamil tutorial, we build Automatic Tool Calling with OpenAI + Python from scratch and understand how the LLM, Python application, and tools work together.

🚀 What You Will Learn

✅ What is Automatic Tool Calling?
✅ Manual vs Automatic Tool Calling
✅ Creating Python tools
✅ Defining tool schemas
✅ Sending tools to an OpenAI model
✅ How the LLM selects the right tool
✅ Understanding function calls and arguments
✅ Executing the selected Python function
✅ Sending the tool result back to the LLM
✅ Generating the final AI response
✅ Complete AI Agent Tool Calling workflow

🔥 Complete Architecture

User
↓
OpenAI LLM
↓
Select Tool
↓
Tool Call + Arguments
↓
Python Application
↓
Execute Tool
↓
Tool Result
↓
OpenAI LLM
↓
Final Answer

💡 Most Important Concept

LLM decides. Application executes.

The LLM does not directly execute your Python function.

Instead:

LLM → Tool Call → Application → Tool Execution → Result → LLM → Final Answer

This architecture is one of the foundations of modern Agentic AI systems.

🐍 Technologies Used

• Python

• OpenAI API

• LLM

• Function / Tool Calling

• AI Agent Architecture

🔗 Learn More

Explore AI, Data Engineering, and technology learning resources:

Teltam.in

📚 Continue the Agentic AI Series

This video continues our practical journey into:

AI Agents → Tool Calling → Memory → RAG → MCP

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