AI developers weigh agents versus function calling for LLM integration

Developers can integrate large language models (LLMs) into software either as pipelines, where the program’s control flow is coded explicitly, or as agents, which receive tools and manage their own control flow. Pipelines are more predictable and cost‑stable but require all context up‑front, while agents are more flexible, can gather additional data on the fly, and handle more complex tasks at the expense of variable latency and cost.

Function calling (also called tool calling) extends LLM capabilities by letting the model output a structured description of a function name and arguments. The application then executes the corresponding Python function, returns the result to the model, and the model produces the final response. Accurate function descriptions are crucial for reliable model decisions, and the loop typically ends with a "tool_calls" finish reason, switching to "stop" once the model has enough information.

Both approaches aim to move LLM reasoning beyond text generation into actionable workflows, with agents favored for hard problems like code generation and function calling providing a clear pattern for safe, deterministic tool use.