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September 16, 2025

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

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Authors

SPSrikanth PeddisettiPeoples Gas (United States)

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Implication

Experimental evaluation shows enhanced adaptability and interpretability in data systems, suggesting broader applications.

Key Points

  • The agentic ai framework achieved an f1-score of 0.91 in credit card fraud detection, indicating robust performance.
  • Integration of four agents improved model adaptability, resulting in a significant reduction of training adaptation time to 85 seconds.
  • The framework outperformed traditional approaches in interpretability and optimization, enhancing explainability for real-world applications.
  • Real-time model drift detection allows for automatic retraining, ensuring continuous optimization and relevance in dynamic environments.

Cite This Study

Srikanth Peddisetti (2025) studied this question.

synapsesocial.com/papers/68d4538731b076d99fa58b83https://doi.org/10.70153/ijcmi/2025.17202
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Also Consider

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  1. 1Agentic AI for Data Engineering: A Systematic Review of Autonomous Pipeline Construction, Optimization, and Governance2026
  2. 2Agentic AI: Autonomous Agents to Cognitive Autonomy2026
  3. 3Agentic AI: Vision and challenges2026 · 1 citations
  4. 4Agentic Data Engineering: Autonomous Agents for Monitoring and Managing Modern Data Pipelines2026
  5. 5Agentic Data Engineering: Autonomous Agents for Monitoring and Managing Modern Data Pipelines2026