DATA-FIRST INTELLIGENCE SELF-SUPERVISED FRAMEWORK FOR SCALABLE AI SYSTEMS USING AUTONOMOUS DATA AGENTS AND SYNTHETIC PIPELINE GOVERNANCE

The convergence of trustworthy AI and scalable data science demands an evolution beyond static, rule-driven learning paradigms. This paper proposes a data-first, self-supervised framework powered by autonomous data agents and synthetic pipeline governance. Through dynamic dataset optimization, drift monitoring, and embedded observability, the framework enables scalable and adaptable AI systems aligned with real-world uncertainties and trust requirements. The study demonstrates significant improvement in model performance and training efficiency using synthetic data, while also embedding integrity checks that fortify reliability and auditability. The resulting architecture facilitates long-term sustainability in intelligent systems and forms a foundation for next-generation trustworthy AI.