September 16, 2025

Agentic AI Meets Data Engineering: Toward Self-Directed, Interpretable, and Balanced Pipelines

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Experimental evaluation shows Agentic AI framework improves model drift detection in data engineering, emphasizing interpretability and adaptability.

The agentic ai framework achieves a notable F1-score of 0.91 during performance evaluations.
Incorporating various agents, the framework effectively interprets inference and detects model drift.
The integration of generative adversarial networks enhances rare event synthesis and minority class representation.
Comparative results demonstrate substantial improvements over baseline automated systems in diverse real-world applications.

Srikanth Peddisetti (2025) studied this question.

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Experimental evaluation shows Agentic AI framework improves model drift detection in data engineering, emphasizing interpretability and adaptability.