How separating logic and search boosts AI agent scalability

Separating logic from inference improves AI agent scalability by decoupling core workflows from execution strategies. This approach addresses a key production challenge: LLM reliability. Since large language models are inherently stochastic, prompts may fail unpredictably. Development teams wrap critical functions to mitigate failures, enabling more robust, scalable AI systems for enterprise deployment.