Context Engineering for AI Agents: Why the Hard Part Isn't the Context Window

DataHub, which sells data-management software, argues that AI agents give confident wrong answers because the company data they read was never checked, and that experts should approve it first.

Companies are connecting AI agents to their data warehouses so that staff can ask business questions in plain language. Lakshay Nasa of DataHub, a maker of data-management software, argues that such agents fail mainly because the data they find is wrong, out of date or contradictory. The article promotes DataHub's own product, and its customer figures are the vendor's.

• Common causes include two teams defining an active customer differently, test tables that look like production tables, and old columns left beside current ones.
• Miro, maker of an online whiteboard, connected an AI coding agent to more than 20,000 datasets. It answered under 40 percent of 900 questions written by Miro's data experts correctly.
• Checked descriptions of the data, better search and curated documentation took Miro's accuracy above 90 percent without changing the AI model.
• In DataHub's product, software proposes definitions from past queries and reports, and experts review, correct and approve them before any agent can use them.

A team building an agent on company data can make someone responsible for checking the data the agent reads, separately from the agent itself. This is an inference, and the article is a vendor's argument for its own product.

18 September 2026: a review of an agent's work is only as good as the reference behind it.