In this article, you will learn the architectural differences between structured outputs and function calling in modern language model systems.

Topics we will cover include:

How structured outputs and function calling work under the hood.

When to use each approach in real-world machine learning systems.

The performance, cost, and reliability trade-offs between the two.

Structured Outputs vs. Function Calling: Which Should Your Agent Use?

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Introduction

Language models (LMs), at their core, are text-in and text-out systems. For a human conversing with one via a chat interface, this is perfectly fine. But for machine learning practitioners building autonomous agents and reliable software pipelines, raw unstructured text is a nightmare to parse, route, and integrate into deterministic systems.

To build reliable agents, we need predictable, machine-readable outputs and the ability to interact seamlessly with external environments.…