Code | Model Boundary as an Architectural Object

This article addresses the instability of LLM agents that occurs when developers rely on the model to make every decision, from query interpretation to output formatting. The author observes that as tasks become more complex, system prompts turn into a collection of contradictory 'patches,' leading to unpredictable system behavior. The core argument is that the boundary between code and the model should be a clearly defined architectural object. Instead of allowing the model to handle everything, developers should explicitly separate areas of responsibility: rigid rules and data output logic should be implemented via code, while the model is reserved for natural language interpretation. This approach improves system stability, predictability, and scalability, transforming agent development from chaotic prompt engineering into a structured, professional engineering process.