Rebuilding Data Engineering with Harness Engineering: A New Paradigm for the Agent Era
As data platforms evolve from serving primarily human users to serving AI agents, the biggest change may not be the tools themselves, but the way data engineering is organized and delivered.
Over the past decade, data engineering has gone through a major wave of specialization. Large, monolithic data platforms have gradually evolved into what is now commonly known as the Modern Data Stack, a composable ecosystem of databases, compute engines, data integration and transformation tools, governance platforms, orchestration systems, and BI solutions. This specialization has dramatically improved engineering efficiency and shifted the industry from building massive, tightly coupled systems toward assembling flexible, modular capabilities.
But as Agentic AI enters the data engineering workflow, a fundamental limitation is becoming increasingly visible: the modern data stack was designed for people, not agents.
The next generation of data platforms will need to solve a different problem. It is no longer enough to help...
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