Tool Calling, Explained: How AI Agents Decide What to Do Next

2026-06-21 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

Tool calling, also known as function calling, transforms Large Language Models (LLMs) from passive text generators into systems capable of triggering external actions and interacting with the real world. This mechanism allows an LLM to decide which external function or API to execute and with specific arguments, such as querying a weather API like Open-Meteo or a currency converter like Frankfurter. The LLM itself does not execute the tool; instead, it returns a structured instruction, which the developer's code then uses to perform the action. The result of this execution is subsequently fed back to the LLM to generate a final, informed response to the user. The article demonstrates single tool calls, the model's ability to select from multiple available tools, and parallel tool calling, where models like gpt-4o-mini can invoke several tools simultaneously for multi-part requests. This capability forms the backbone of agentic AI workflows, enabling LLMs to perceive, process, and act towards a goal.

Key takeaway

For AI Engineers building interactive LLM applications, understanding tool calling is essential for moving beyond static text generation. You should design your LLM integrations to utilize external APIs, allowing your models to perform real-world actions like fetching live data or converting currencies. This approach transforms your LLM into a capable agent, enabling it to handle complex, multi-step user requests more efficiently and accurately by orchestrating external functionalities.

Key insights

Tool calling enables LLMs to trigger external actions by deciding which function to call and with what arguments, forming agentic AI workflows.

Principles

• LLMs decide tool calls; external code executes them.
• Clear tool descriptions are crucial for model selection.
• Agentic systems perceive, process, and act on goals.

Method

The tool calling loop involves the user submitting a message, the LLM deciding on a tool and arguments, external code executing the tool, and the result being fed back to the LLM for a final response.

In practice

• Define tools with clear descriptions and parameter schemas.
• Implement external functions for APIs like Open-Meteo or Frankfurter.
• Append tool calls and results to message history for context.

Topics

• Tool Calling
• Function Calling
• LLM Agents
• API Integration
• Agentic Workflows
• Parallel Tool Calling

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.