Self-Evolving Analytics Pipelines for Reliable AI-Augmented Software Systems

Abstract

Reliable and accountable AI-augmented software systems are crucial for their successful deployment and use. For trustworthy operation at runtime, self-evolving analytics pipelines can enhance monitoring, quality control, and governance of AI applications. A systematic analysis delivers a rigorous foundation and identifies key aspects of self-evolving analytics pipelines that contribute to reliability and accountability. The resulting requirements encompass evaluation-oriented properties, techniques for continuous learning of ML pipelines, and mechanisms for runtime monitoring with data monitoring and quality dashboards. This extended version formalises those requirements in sixteen numbered equations covering reliability, availability, three distinct classes of drift, retraining economics, and accountability; specifies four algorithms governing the self-evolution control loop, joint-condition drift alerting, safe canary promotion, and accountability evidence assembly; and grounds the argument in 2023–2025 incident and transparency data.Reliable AI systems must fulfil numerous conditions, yet evidence illustrates that not all aspects are met concurrently. Empirical studies have established evaluation criteria for model quality, though mechanisms for reliable operation during production are less mature. Supply-chain principles indicate that monitoring and analytics become paramount in production. Reliable operation encompasses continual control in streaming environments, online adaptation of AI models to data drift, and external feedback to alert systems.