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LLM & Agentic
02

How LLMs Actually Call Tools

LLMs don’t execute code — they emit structured text that your system interprets. Understanding this mechanism changes how you design agents.

KEY INSIGHT

Tool calling is an illusion of action. The model outputs a JSON object that looks like a function call. Your code parses it, executes the real function, and feeds the result back. The model never touches your APIs, databases, or file system directly.

The Runtime Loop

Every tool-calling system follows the same cycle, regardless of provider (Claude, GPT, Gemini):

TOOL-CALLING LOOP
  1. You send the user message + tool schemas in the API request
  2. Model decides whether to respond directly or call a tool
  3. Model emits a tool_use block — structured JSON with tool name + arguments
  4. Your code validates, executes the real function, handles errors
  5. You send back a tool_result message with the output
  6. Model reads the result and either responds to the user or calls another tool

Steps 2–6 can repeat multiple times in a single conversation turn. This is the agentic loop — the model keeps calling tools until it has enough information to answer.

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