Continuous Evaluation & Observability for Enterprise AI Agents: A Unified Framework for LLM and ML Systems

Abstract

The emergence of AI agents combining large language models with traditional ML components has created evaluation challenges that existing monitoring approaches cannot adequately address. This article presents a unified continuous evaluation framework designed for hybrid AI agent systems in enterprise environments. The framework integrates telemetry collection, drift detection, safety assessment, and business outcome measurement into a cohesive architecture. Through systematic analysis of framework components and implementation patterns, this work establishes theoretical foundations for reliable AI agent evaluation while addressing technical performance and business alignment requirements. The unified architecture incorporates reinforcement learning from human feedback, synthetic test generation, and advanced observability infrastructure to create a foundation for enterprise AI deployment. This framework addresses gaps in current evaluation methodologies by providing structured approaches to semantic assessment, multi-turn consistency validation, and business outcome correlation for AI agent systems.