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AI series · Part 4: Agentic Automation

Tool Calling: How LLMs Use Your APIs Without Running Code

Urnish Tech·Oct 12, 2026· 3 min read

With tool calling, the LLM picks a tool from a menu and asks for it. Your code runs it and hands back the result. The model never touches your systems directly.

With tool calling, we give the LLM a menu of tools, each with a name, a purpose and its inputs. The LLM asks for one ("call lookup_employee for Rahul"), our code runs it, and the result goes back to the LLM.

The restaurant analogy

  • You (the LLM) order from the menu.
  • The waiter (our code) takes the order to the kitchen (the tool).
  • The waiter brings back the food (the result).
  • You never enter the kitchen.

A simple example

User: "What's the weather in Pune?"

text
LLM  → requests get_weather("Pune")
App  → calls the weather API → {"temp": 28, "sky": "sunny"}
LLM  → "It's 28°C and sunny in Pune."

The LLM never runs code itself. It outputs a structured tool request, my application runs it and returns the result.

- The short version

Next in the GenAI series: MCP (Model Context Protocol): The USB-C Cable for AI

Start from the top: GenAI Quick Answers and Series Guide

We deploy these patterns across production engagements in fintech, healthcare, and eCommerce. Talk to a senior engineer about how they apply to your platform.

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