TY  - CONF
T1  - Transparency and Auditability in AI-Driven Data Pipelines: Toward Explainable Agentic ETL Systems
A1  - Brandas, Claudiu
A1  - Plesas, Mihai
A1  - Gard, Sébastien
A1  - Reychav, Iris
A1  - McHaney, Roger
A1  - Sharma, Madhav
TI  - The 13th International Conference on Information Technology and Quantitative Management
T3  - Procedia Computer Science
Y1  - 2026
VL  - 287
SP  - 1093
EP  - 1103
PB  - Procedia Computer Science, Elsevier B.V
UR  - https://www.sciencedirect.com/science/article/pii/S1877050926027882
M2  - doi: https://doi.org/10.1016/j.procs.2026.08.152
KW  - Agentic AI
KW  - Auditability and provenance
KW  - Data pipeline transparency
KW  - ETL
KW  - Explainable AI
KW  - Low-code workflow orchestration
N2  - The growing adoption of large language model (LLM)-driven agentic systems is reshaping how organizations automate data workflows in management and analytics contexts. However, their increasing complexity introduces critical challenges in transparency, explainability, and operational oversight, particularly within extract-transform-load (ETL) pipelines where routing decisions, data interpretation, and cleaning actions may be difficult to trace or audit. This study proposes and evaluates an auditable multi-agent ETL architecture implemented on a low-code automation platform. The system employs a central orchestrator coordinating four specialized agents—Extraction, Validation and Quality Assurance, Cleaning and Transformation, and Analytics—supported by OpenAI GPT-5-mini. The pipeline integrates data from Google Sheets, generates visualizations via QuickChart, and embeds auditability mechanisms including transformation logs, QA reports, and non-destructive corrections. Using a design science methodology, the system was iteratively developed and empirically evaluated for transparency, performance, and failure modes. Experimental runs completed in 5–7 minutes, demonstrating effective anomaly detection across formatting, completeness, and domain rules, while applying conservative fixes and escalating uncertain cases to human oversight.

The findings highlight the importance of observability, verification, and tamper-evident audit structures in agentic ETL systems, and underscore the need for scalable, transparent AI-driven data pipelines.
ER  -