Reliability over Unfettered Autonomy: Advocating for Deterministic Orchestration in Large Language Model Tool Integration

Abstract

The advent of large language models capable of using external tools promises unprecedented automation, but granting LLMs full control over tool selection and execution introduces significant risks of hallucination and unpredictability. Practical experience reveals challenges where LLMs invoke non-existent tools, misinterpret parameters, or fail to adhere to structured output formats necessary for successful tool interaction. This article advocates for deterministic orchestration as an alternative approach. Instead of granting LLMs primary decision-making authority over tool use, this methodology employs conventional programming logic to manage workflows. Functions are invoked deterministically based on the application's state or structured interpretation of user requests, with outputs fed back to the LLM for higher-level tasks like synthesizing information or generating natural language responses. This method sacrifices some agent autonomy for enhanced predictability, control, reduced hallucination risk, and easier debugging.