B.R.I.C.K.: Bounded, Reliable, Immutable Cross-Layer Kernels — A Deterministic Architectural Governance Framework for Human-AI Hybrid Software Engineering Across Distributed and Air-Gapped Environments

Modern software engineering operates at an unprecedented crossroads: while human engineers face cognitive limitations, exhaustion, and memory decay when navigating sprawling enterprise codebases, autonomous and generative AI coding agents introduce stochastic drift, hallucinated dependencies, untracked orphan artifacts, and silent architectural erosion. This paper proposes the B.R.I.C.K. Framework and Deterministic Sentinel Engine—a formal, end-to-end architectural governance model. B.R.I.C.K. restructures enterprise codebases into Bounded, Reliable, Immutable Cross-Layer Kernels characterized by: (1) Bounded domain context adhering strictly to Domain-Driven Design (DDD); (2) Reliable contractual integrity with mandatory business rules mapped directly to automated regression suites; (3) Immutable cryptographic integrity enforced through content-addressed SHA-256 fingerprinting; (4) Cross-layer governance spanning frontend components, backend logic, and live multi-schema database objects; and (5) Kernels of strict, mutually exclusive single ownership that eradicate orphaned or unassigned files. The framework features a dual operational mode: an Enterprise Distributed Mode leveraging central Git trees and live relational catalogs (PostgreSQL), and an Air-Gapped / Offline Mode operating independently without internet, remote servers, or Git dependencies via a local shadow stash and embedded catalogs (SQLite). Empirical evaluation on an enterprise-grade production municipal administration system (45 kernels, 301 frontend files, 128 database procedures, 90 tables) demonstrates that the deterministic sentinel intercepts 100% of unauthorized structural and schema mutations, eliminates code regression, and incurs less than 4% build-pipeline execution overhead (1.14s local verification). This establishes a mathematically verifiable foundation for safe, collaborative human-AI software engineering.