Hybrid Architecture of Intelligent Systems with a Deterministic Core: from Concept to Prototype

Abstract

The rapid development of large language models and autonomous intelligent agents has significantly expanded the capabilities of natural language processing and decision support. However, practical implementation reveals fundamental limitations, particularly in tasks requiring computational robustness, reproducibility of results, and strict information consistency. These issues are particularly critical in fields such as engineering, geometry, and educational systems, where plausible but inaccurate responses ("hallucinations") and unstable behavior undermine system trust. This paper proposes a hybrid intelligent architecture with a deterministic core to address these challenges. Unlike fully autonomous systems, the proposed approach decouples functions: an adaptive agent handles user interaction and its interpretation, while a stationary deterministic core provides robust computation, logical consistency, and graphical display. The architecture introduces a clear distinction between the development phase, which allows for iterative improvement, and the operational phase, characterized by a fixed core that guarantees reproducible and verifiable results. By providing protocol-based interaction between the agent and the deterministic core, the system ensures that all generated output—text, computational, and graphical—remains consistent and adheres to the underlying domain model. This hybrid structure combines the flexibility of modern intelligent agents with the precision and reliability of formal deterministic models, offering a robust foundation for mission-critical intelligent applications.