Problem
Research questions and friction points this paper is trying to address.
data warehouse delivery
production automation
dependency-aware orchestration
artifact lifecycle control
platform evolution adaptation
SiriusDeliver: Automating Data Warehouse Delivery at Tencent
🤖 AI Summary
Enterprise data warehouse task delivery involves complex workflows that existing large models and agents struggle to support in production settings due to insufficient dependency awareness, lifecycle management, and platform evolution capabilities. This work proposes the first end-to-end automated delivery agent framework, which coordinates hierarchical agents to orchestrate warehouse-specific skills, validates artifacts before and after execution through lifecycle-aware controls, and employs a trajectory-driven skill evolution mechanism for continuous optimization. Key innovations include a dependency-aware orchestration scheme, artifact governance within closed-loop execution, and a real-trajectory-based skill refinement approach. Deployed at scale on Tencent Cloud WeData, the system serves 3,600 monthly active users, supports 18,240 delivery sessions per month, achieves an end-to-end success rate of 87.2%, and enables 73.5% autonomous submissions. A/B testing demonstrates a reduction in median delivery time from 228 to 23 minutes and engineer effort from 95 to 11 minutes.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
delivery automation
artifact lifecycle control
trace-driven skill evolution
dependency-aware orchestration
warehouse task submission
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