Alibaba Cloud Launches ADA: AI-Native Big Data Service for Conversational Delivery

Enterprises today face a gap between large language models that can generate SQL and the reality of delivering reliable, production-ready data results. The article identifies three core problems: (1) generated code often contains hallucinations or logic errors because generic agents lack deep understanding of business definitions and complex table relationships; (2) workflows remain fragmented — users must still switch consoles, configure permissions, paste code, and debug errors manually; (3) external agents cannot perceive underlying engine health (resource bottlenecks, data skew) nor inherit existing enterprise permission systems such as RAM and multi-tenant isolation, creating compliance risks.

ADA: An AI-Native Big Data Service

Alibaba Cloud's ADA (AI-native Data Analytics) positions itself as a next-generation data productivity platform that fuses agent technology with big data and AI engines (MaxCompute, Hologres, Flink, EMR, DataWorks). Its goal is to let users complete complex big data tasks through natural language conversation, accelerating data-driven decisions.

Core Architecture and Three Key Capabilities

Multi-Agent Collaborative Orchestration: AdaWork acts as the master agent (Master Agent), handling intent understanding, task routing, and automatic coordination. It seamlessly schedules specialized agents from each underlying engine to accomplish cross-engine data delivery tasks.

Enterprise-Grade Knowledge Engine: Built-in semantic graph capabilities (same as DataWorks) turn enterprise data assets, business semantics, and analytical experience into a unified cognitive hub.

High-Freedom Customizable Agents: Supports both low-code and high-code modes, allowing enterprises to plug in mainstream agent frameworks and build dedicated agents tailored to their business scenarios.

Four Core Advantages Reshaping the Data Workflow

1. Ultra-Simple Interaction: Zero Barrier, Ask and Analyze, Conversation Delivers

Pre-built, ready-to-use data agent solutions let business users initiate analysis, development, and governance tasks via natural language — no SQL or complex engine operations required. The entire flow from requirement understanding, task decomposition, cross-engine invocation, to final result delivery is autonomously closed-looped by the agent.

2. Multi-Agent Collaboration: Multi-Role Orchestration, End-to-End Business Delivery

The Ada master agent serves as a "super brain" that uniformly understands intent and breaks down tasks. Within a unified workspace, it seamlessly schedules engine-level agents. Built-in professional roles — data analyst, semantic modeling expert, data engineering expert, intelligent O&M expert — collaborate to cover the full business loop from insight to governance, eliminating the fragmentation of switching between multiple consoles.

3. Trustworthy and Traceable: Unified Semantic Knowledge Hub, Every Conclusion Verifiable

Semantic models, metric calculation logic, table relationships, and analytical experience are沉淀 as platform-level knowledge assets (knowledge graph, business semantic library, memory library). This ensures all agents and teams work from a single source of truth, eliminating metric conflicts. Every analytical conclusion carries citation provenance, preventing ambiguity and making data decisions verifiable and trustworthy.

4. Native Perception: Engine-Embedded Agents, Deep System Insight

Unlike traditional generic agents that fetch data via APIs, ADA's agents are embedded directly inside the data systems. They can perceive system runtime state in real time, autonomously diagnose performance and health issues, and propose optimization plans — all without human intermediation. This native architecture gives agents unmatched depth of perception, precise diagnostics, and real-time responsiveness.

Empowering Every Role to Become a Data Expert

Business users: Break technical barriers, achieve "what you think is what you get." Compress data value acquisition cycle from days to minutes, enabling on-demand data-driven business growth.

Data engineers/analysts: Freed from repetitive low-value work, they can focus on high-value semantic modeling, complex workflow orchestration, and deep business insights.

Enterprise managers: Build an enterprise-exclusive "data cognitive asset." Through a unified knowledge hub and strict permission/audit mechanisms (inheriting RAM permissions, full operation traceability), maximize data asset value while ensuring enterprise-grade security and compliance.