Separating logic from inference is a critical approach to enhance the scalability of Artificial Intelligence (AI) agents. This strategy enables the decoupling of core workflows from execution methods, a vital step in the transition from generative AI prototypes to production-ready agents.
This transition often encounters engineering challenges, particularly concerning reliability. Large Language Models (LLMs) are inherently stochastic, meaning a prompt that works successfully once may yield different results or fail on subsequent attempts. To mitigate this unpredictability and ensure consistency, development teams commonly encapsulate and isolate core business logic from the LLM's inference process, thereby creating a more stable and scalable system.