DataWorks Data Agent Powers AI‑Driven Data Development: 2‑3× Faster and 80% Automation with SuperETL

DataWorks Data Agent, the first deep‑co‑creation user of the platform, combines ten years of logistics data‑warehouse experience with a proprietary SuperETL intelligent agent system. By tightly integrating industry knowledge, development standards, and quality metrics into an executable skill set, the solution boosts data‑development efficiency by 2‑3× and achieves over 80% AI‑driven task completion, turning a traditional "tool‑assisted" workflow into an "agent‑driven" paradigm.

Current pain points identified by the team include fragmented processes across multiple engines (Aone, DataWorks, Flink, Paimon, FBI), low collaboration efficiency, ineffective standards that remain undocumented, and poor quality control due to insufficient testing, DQC coverage, and cumbersome code reviews. These issues lead to high coordination costs and exponential downstream maintenance effort.

Solution architecture introduces nine finely‑grained Skills (e.g., using-superetl, etl-deepresearch, etl-debugging, etl-brainstorming, etl-writing-plans, etl-validated-coding, etl-review-and-release, etl-dispatch-parallel, etl-subagent-driven) orchestrated by a skill‑router and enforced by four hook stages ( SessionStart, PreToolUse, PostToolUse, SessionEnd). The Hooks provide production‑grade safety checks, such as blocking releases unless a checklist is verified.

Knowledge resources are organized into six repositories (spec, checklists, templates, guides, techniques, wiki) that store industry‑specific schemas, naming conventions, model designs, and operational best practices, enabling the agent to retrieve authoritative information during execution.

Practical workflow example : adding the field sign_on_time_rate to the dws_lgt_order_1d table. The process proceeds through six steps—intent routing, deep research, brainstorming, plan writing, validated coding, and secure release—each driven by a specific skill and validated by hooks. Confidence thresholds (30‑90%) guide the transition between steps, and checklist tracking ensures compliance before production deployment.

Outcome – the case demonstrates a complete end‑to‑end AI‑augmented pipeline: from natural‑language request to schema retrieval, logical design, plan generation, code development, automated testing, and gated release. The approach reduces manual effort, enforces standards, and provides traceable, repeatable processes.

Future outlook envisions a shift from monolithic data‑warehouse projects to a data‑grid architecture (ODS‑CDM‑ADM) enriched with AI skills, AI‑generated reports, and system apps that execute data‑driven business actions. Knowledge graphs (WIKI) will embed table definitions, concepts, and metrics, making large‑model usage a governed part of the data platform.

In summary, DataWorks Data Agent and SuperETL illustrate an AI‑era data‑development paradigm where AI‑executable skill sets, safety hooks, and unified CLI tooling replace ad‑hoc scripting, delivering scalable, high‑quality data products.