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? Image by Editor 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.…