AI RESEARCH
kRAIG: A Natural Language-Driven Agent for Automated DataOps Pipeline Generation
arXiv CS.AI
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ArXi:2603.20311v1 Announce Type: cross Modern machine learning systems rely on complex data engineering workflows to extract, transform, and load (ELT) data into production pipelines. However, constructing these pipelines remains time-consuming and requires substantial expertise in data infrastructure and orchestration frameworks. Recent advances in large language model (LLM) agents offer a potential path toward automating these workflows, but existing approaches struggle with under-specified user intent, unreliable tool generation, and limited guarantees of executable outputs.