Database-Governed AI Architecture Ensures LLM-Agnostic Compliance and Auditability

A novel AI governance architecture proposes a database-centric approach to building AI applications, emphasizing auditability, LLM-agnosticism, and compliance with regulations like the EU AI Act. This pattern positions a single database as the owner of the entire AI process, encompassing steps, functions, skills, messages, and data evolution, while the large language model (LLM) operates as a stateless semantic engine.

Inspired by Claude Code's configuration model, this architecture applies principles of "structure over intelligence" and "config over code" to full applications. Key tenets include: the database defining every step and interaction, ensuring the LLM never autonomously decides the next action; a "Don't train — certify" philosophy for skills, allowing runtime configuration changes without retraining or redeployment; and explicit data evolution mapping, where the output of step N becomes the input for step N+1, creating a clear value chain of information.

This design ensures that every LLM call is constrained, testable, and fully auditable. Expected inputs, detailed prompts, and skill configurations lead to structured outputs, with every call subjected to Test-Driven Development (TDD) using golden examples. Furthermore, all data—input, structured output, and a full audit trail—is persistently stored in the database, eliminating reliance solely on the LLM's context window.

A significant advantage of this architecture is its LLM-agnostic nature. The governance model resides within the database, not the specific LLM, allowing developers to swap LLM providers without altering the core application logic or governance framework. This flexibility is crucial in a rapidly evolving AI landscape.

The side effects of this database-governed approach are inherent benefits for compliance and risk management. It provides complete auditability, enabling reconstruction of every prompt, response, and decision from the database state at any point in time. For EU AI Act and model risk compliance, it offers clear process definition, data lineage, and test evidence for every LLM call. Additionally, field-level data evolution tracking supports GDPR compliance by precisely identifying what personal data is held, its location, and its processing path. This architecture offers a robust blueprint for developing responsible, transparent, and legally compliant AI systems.
[Transparency Statement: This analysis was generated by an AI model based on the provided source material. No external data was used. The model aims for factual accuracy and unbiased interpretation.]