Behavior Rule Architecture: Rule-Based Governance of AI System Behavior

The rapid adoption of AI coding assistants has introduced unprecedented challenges in controlling generated behavior. Current approaches—including prompt engineering, custom instructions, and repository-level rules files—rely on natural language that lacks semantic precision and enforcement capability. These methods conflate intent optimization with behavior enforcement, leading to semantic drift, context overload, and rule conflicts without systematic governance mechanisms. This paper introduces Behavior Rule Architecture (BRA), a structured framework for defining and managing AI execution behavior through normative rules. BRA establishes a clear separation between Normative Natural Language (NNL) for intent expression and Rule Normative Language (RNL) for behavior enforcement using MUST/MUST NOT semantics. The architecture comprises three layers: (1) Rule Language Layer for semantic transformation from intent to enforceable rules, (2) Rule Architecture Layer for atomic rule definition with Policy (MUST) and Constraint (MUST NOT) classifications based on observability differences, and (3) Governance Asset Layer for organizing reusable rule libraries through Decision Behavior Normative Categories and composable Application Rulesets. BRA addresses three fundamental challenges in AI behavior governance: formalization of behavioral constraints, auditability through structured metadata, and reusability via shared rule libraries. The framework enables the transition from ad-hoc prompt engineering to systematic behavior rule management, establishing the engineering foundation for controllable, auditable, and governable AI systems.