mlsanityData to 7 Oct 2026

SiriusDeliver: Automating Data Warehouse Delivery at Tencent

arXiv 2608.09185 ↗First posted 10 Aug 2026cs.DB cs.AI cs.SEcited by 0

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Abstract (arXiv)

Enterprise data warehouses (DWs) support business-critical analytics, but warehouse task delivery remains a complicated production process involving context retrieval, workflow configuration, code generation, platform submission, and failure diagnosis. Although large language models (LLMs) and coding agents have improved software development, they are insufficient for production DW delivery, which requires dependency-aware orchestration, lifecycle-aware artifact control, and continuous adaptation to evolving platform practices. We present SiriusDeliver, an end-to-end delivery automation agent for production warehouse task submission. SiriusDeliver integrates three components: a hierarchical delivery agent that orchestrates warehouse skills, an artifact lifecycle control module that verifies and revises artifacts before and after platform execution, and a trace-driven skill evolution mechanism that maintains reusable skills from delivery trajectories. We evaluate SiriusDeliver through offline datasets and large-scale production deployment on Tencent Cloud WeData. Offline experiments on real-world warehouse delivery cases show that SiriusDeliver improves delivery success and automation efficiency over representative baselines. During a two-month deployment across 6 business teams and 4 warehouse task types, SiriusDeliver served 3,600 monthly active users and supported 18,240 delivery sessions, achieving an 87.2% end-to-end success rate and a 73.5% autonomous submission rate. A one-month A/B test shows that SiriusDeliver reduces median delivery time from 228 to 23 minutes and engineer effort from 95 to 11 minutes, while maintaining comparable final delivery success.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 31 references that are in the map. First posted on arXiv 10 Aug 2026.

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References · 11

  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  3. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
  4. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  6. 2023ExpeL: LLM Agents Are Experiential Learnerscited by 814
  7. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  8. 2024Data Interpreter: An LLM Agent For Data Sciencecited by 222
  9. 2024CHESS: Contextual Harnessing for Efficient SQL Synthesiscited by 164
  10. 2023TaskWeaver: A Code-First Agent Frameworkcited by 85
  11. 2026SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platformscited by 1

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