Context Engineering on DataAgentX

The 2W1H of the Semantic Layer for Agents: Why It's Needed, What It Is, and How to Build It

Fri, 23 Oct 2026 00:00:00 +0000

Does connecting to a database and generating SQL guarantee that an Agent will answer business questions correctly?

When asked the same question, “What was last quarter’s revenue?”, different tools yield different answers: a BI tool might include taxes and shipping costs; a Notebook might fail to deduct discounts and returns; finance may recognize revenue based on shipment dates, while the Agent is still guessing how tables relate.

Inside OpenAI’s Data Agent: Context Before SQL

Fri, 18 Sep 2026 00:00:00 +0000

Two tables both contain user_id; why do they produce different active-user counts? Enterprise analytics often fails before SQL generation: the system chose the wrong table, timestamp, filter, or definition.

OpenAI’s January 2026 account of its internal data agent centers the design on context engineering. The article’s snapshot described more than 3,500 internal users, 600 PB of data, and roughly 70,000 datasets. The more transferable lesson is how the system narrows that search space.

The Data Agent Access Layer: Finding the Right Data Before Querying

Tue, 08 Sep 2026 00:00:00 +0000

“Why did new-customer conversion fall this month?” A warehouse may contain a production funnel model, a similarly named test table, and a spreadsheet from last quarter’s campaign. All three are reachable. All three have plausible columns. If the agent chooses the wrong one, polished SQL and charts only make the error more persuasive.

Before query generation, the access layer must answer three questions: What may this identity access? What should this task use? Is the selected evidence sufficient for the claim? It is not a pipe that dumps every schema into a prompt. It is a permission-aware discovery and context-routing system.