Core capabilities
Context retrieval, semantic alignment, planning, tool execution, validation, and governance form the complete capability chain.
Read the definition and boundariesA field guide to data agents
Understand Data Agents. Build data systems you can trust.
DataAgentX is an independent publication covering the architectures, products, industry shifts, and operating practices behind governed, verifiable data work.
TRUSTED EXECUTION PATH
- 01Understand
- 02Ground
- 03Execute
- 04Verify
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What is a Data Agent?
A Data Agent is an intelligent system built to complete data work. It turns a business question into an executable plan, uses governed data and analytics tools, and returns results with sources, assumptions, and validation evidence. It is broader than natural-language-to-SQL and more operational than chat-based BI.
Reference architecture
From task entry to delivery and continuous learning, seven layers cover semantics, planning, controlled execution, verification and repair, with governance throughout.
Explore the seven-layer architectureProduction readiness
Executable SQL does not guarantee a trustworthy answer; teams must also test metric definitions, permission inheritance, evidence, and recovery paths.
Build an evaluation systemLatest coverage
News, products, and technical insight
Data Agent Weekly: Implementation Challenges at DACon26
October 18–24, 2026: Six engineering challenges distilled from 19 sessions at DACon26, alongside updates on DataBuddy private deployment, DatI, ontology-enhanced research, and progress from NVIDIA, Hex, Snowflake, and Plotly.
The 2W1H of the Semantic Layer for Agents: Why It's Needed, What It Is, and How to Build It
Starting from the three pillars of DACon 2026, this article explains what unified metadata, open semantic layers, and Ontology each address, why context expires, and which real-world problems should serve as the starting point for construction.
Before Adjusting a Fee Rate, Identify Which Transactions Will Be Affected
Recalculate payment fee rate changes using two public hard problems from DABstep: how conditions map to MCC codes, how the affected transaction set is determined, why fixed amounts offset in the differential, and how individual differentials are independently verified.
Tencent Cloud DataBuddy: The First Signs of RSI in Data Agents
Synthesizing the launch event to outline DataBuddy's four task categories, three foundational pillars, and two integration pathways. It explains why context updates for engineering governance and business feedback currently serve only as prerequisites for self-evolution rather than representing an achieved capability.