Methodological foundations of AI observability for enterprise LLM applications

Abstract

The article describes the methodological foundations of AI observability for large language models integrated into enterprise environments. The relevance is determined by the adoption of LLMs in business alongside the emergence of specific risks: hallucinations, data leaks, and uncontrolled cost escalation that are not covered by traditional monitoring tools. The scientific novelty lies in proposing a multi-level framework that systematizes observability metrics across four dimensions: quality and semantics, cost and performance, security and privacy, responsibility and ethics. The study identifies the limitations of classical MLOps approaches as applied to LLMs and analyzes contemporary methods for detecting anomalous model behavior. Special emphasis is placed on coupling automated metrics with human feedback mechanisms. The purpose of the study is to construct a holistic methodology for designing LLM observability systems. To achieve this goal, methods of systems and comparative analysis, as well as conceptual architectural modeling, are employed. In conclusion, the practical significance of the framework is demonstrated for minimizing risks and increasing the return on investment from LLM applications. The findings presented in this work will be of interest to project managers in Data Science, MLOps engineers, and AI systems architects.