kRAIG: A Natural Language-Driven Agent for Automated DataOps Pipeline Generation

• URL: http://arxiv.org/abs/2603.20311v1
• Date: Thu, 19 Mar 2026 17:53:00 GMT
• Title: kRAIG: A Natural Language-Driven Agent for Automated DataOps Pipeline Generation
• Abstract summary: We introduce kRAIG, an AI agent that translates natural language specifications into production-ready KubeReason Pipelines (KFP)<n>To resolve ambiguity in user intent, we propose ReQuesAct, an interaction framework that explicitly clarifies intent prior to pipeline synthesis.<n>Our framework achieves a 3x improvement in extraction and loading success and a 25 percent increase in transformation accuracy compared to state-of-the-art agentic baselines.
• Score: 0.5833117322405447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We introduce kRAIG, an AI agent that translates natural language specifications into production-ready Kubeflow Pipelines (KFP). To resolve ambiguity in user intent, we propose ReQuesAct (Reason, Question, Act), an interaction framework that explicitly clarifies intent prior to pipeline synthesis. The system orchestrates end-to-end data movement from diverse sources and generates task-specific transformation components through a retrieval-augmented tool synthesis process. To ensure data quality and safety, kRAIG incorporates LLM-based validation stages that verify pipeline integrity prior to execution. Our framework achieves a 3x improvement in extraction and loading success and a 25 percent increase in transformation accuracy compared to state-of-the-art agentic baselines. These improvements demonstrate that structured agent workflows with explicit intent clarification and validation significantly enhance the reliability and executability of automated data engineering pipelines.

Related papers

• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems [82.01232437066487]
  AgentX is a production-deployed multi-agent system that restructures this production function.<n>It autonomously generates, implements, evaluates, and learns from recommendation experiments.<n>AgentX orchestrates four tightly coupled stages in a closed loop.
  arXiv  Detail & Related papers  (2026-06-25T10:42:28Z)
• SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines [7.868559688632956]
  We demonstrate SemPipes, a programming model that extends machine learning pipelines with semantic data operators.<n>For the semantic operators, SemPipes synthesizes specialized implementations at pipeline training time.<n>We show how semantic operators enable controllable, optimizable, and practical integration of LLMs into ML pipeline development.
  arXiv  Detail & Related papers  (2026-06-12T11:40:09Z)
• DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving [55.85201665674226]
  High-quality training data is essential to large language models (LLMs)<n>Existing automatic data preparation methods rely on predefined pipelines or customized human instructions.<n>We introduce DataEvolver, the first self-evolving data preparation system that automatically constructs pipelines to transform raw data into high-quality data.
  arXiv  Detail & Related papers  (2026-06-05T07:44:34Z)
• Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
  Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
  arXiv  Detail & Related papers  (2026-03-11T09:23:20Z)
• AgentSkiller: Scaling Generalist Agent Intelligence through Semantically Integrated Cross-Domain Data Synthesis [30.512393568258105]
  Large Language Model agents demonstrate potential in solving real-world problems via tools, yet generalist intelligence is bottlenecked by scarce high-quality, long-horizon data.<n>We propose AgentSkiller, a fully automated framework synthesizing multi-turn interaction data across realistic, semantically linked domains.
  arXiv  Detail & Related papers  (2026-02-10T03:21:42Z)
• Autonomous Data Processing using Meta-Agents [2.3732259124656907]
  We present textbfAutonomous Data Processing using Meta-agents (ADP-MA), a framework that dynamically constructs, executes, and iteratively refines data processing pipelines.<n> ADP-MA emphasizes context-aware optimization, adaptive workload partitioning, and progressive sampling for scalability.<n>We demonstrate ADP-MA through an interactive demo that showcases pipeline construction, execution monitoring, and adaptive refinement across representative data processing tasks.
  arXiv  Detail & Related papers  (2026-01-30T20:58:17Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• FABRIC: Framework for Agent-Based Realistic Intelligence Creation [3.940391073007047]
  Large language models (LLMs) are increasingly deployed as agents, expected to decompose goals, invoke tools, and verify results in dynamic environments.<n>We present a unified framework for synthesizing agentic data using only LLMs, without any human-in-the-loop supervision.
  arXiv  Detail & Related papers  (2025-10-20T18:20:22Z)
• Classifier-Augmented Generation for Structured Workflow Prediction [5.92079054629498]
  We propose a system that translates natural language descriptions into executables.<n>It automatically predicts both the structure and detailed configuration of the flow.<n>This is the first system with a detailed evaluation across stage prediction, edge layout, and property generation for natural-driven authoring.
  arXiv  Detail & Related papers  (2025-10-10T18:38:25Z)
• ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction [84.90394416593624]
  Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions.<n>Existing simulation-based data generation methods rely heavily on costly autoregressive interactions between multiple agents.<n>We propose a novel Non-Autoregressive Iterative Generation framework, called ToolACE-MT, for constructing high-quality multi-turn agentic dialogues.
  arXiv  Detail & Related papers  (2025-08-18T07:38:23Z)
• ACT: Bridging the Gap in Code Translation through Synthetic Data Generation & Adaptive Training [1.4709455282157278]
  Auto-Train for Code Translation (ACT) aims to improve code translation capabilities by enabling in-house finetuning of open-source Large Language Models (LLMs)<n>ACT's automated pipeline significantly boosts the performance of these models, narrowing the gap between open-source accessibility and the high performance of closed-source solutions.<n>Our results demonstrate that ACT consistently enhances the effectiveness of open-source models, offering businesses and developers a secure and reliable alternative.
  arXiv  Detail & Related papers  (2025-07-22T11:35:35Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.