Accelerating Data Engineering Productivity with Agent-Based Automation and Natural-Language Interface

Abhishek Anand

Citation: Abhishek Anand, "Accelerating Data Engineering Productivity with Agent-Based Automation and Natural-Language Interface", Universal Library of Innovative Research and Studies, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This article presents a way to accelerate data engineering productivity with the help of agent-based automation and a natural language interface, driven by the exponential growth of the global Datasphere and all that extra manual work required for integration through heterogeneous sources, plus dynamically changing APIs, which slows down changes making their way to production and increases operational costs. The paper aims to construct an architecture comprising four layers: a semantic gateway, an agent manager, a unified execution environment, and a closed-loop feedback mechanism that feeds into validation. These requirements naturally map to dynamic pipelines for self-configuring data processing with no static DAG script hand-coding involved. It therefore suggests implementing the ReAct and AutoGen agent patterns for coordinating multiple users via LLM agents in dynamic operation-and-tool-selection workflows. The patterns introduce self-healing through the automatic diagnosis and correction of failures based on traces of reasoning from telemetry collected so far, without involving a full redeployment cycle. This also demonstrates that the pattern reduces manual intervention in assurance levels through increased agent autonomy and multi-agent scheme composability, going beyond classic DAG structures. Operationally, agents function as an execution-time extension of the REQUEST requirements model—capturing intent (R,E,Q) and converting it into units, events, and scoped trade-offs for dynamic orchestration. Natural language interfaces reduce the iteration between specifying what is needed and finalizing code, while increasing velocity in bringing new participants on board, as well as lowering technical barriers for domain experts. The article will be useful for data engineering researchers and practitioners, as well as automation system developers and solution architects.


Keywords: Data Engineering, Agent-Based Automation, Natural-Language Interface, Productivity, Self-Healing Pipelines.

Download doi https://doi.org/10.70315/uloap.ulirs.2025.0204008