I just sat in a room full of data engineers the other week who were worrying about AI automating them out of work the same way auto manufacturing in Detroit was upended half a century ago. All AI. All the time. That’s what technology professionals are talking about. Data scientists, data engineers, and data architects are right to sound an alarm at that. Using AI to solve and automate data problems all the way at the beginning of the pipeline is an obvious use case of agentic engineering in data. Shifting AI left for automation. That looms as a threat to data engineering positions who own the pipeline underlying the architecture and deliverables. It’s a discussion that we can no longer avoid. In all fields, AI is looming, bringing with it new risks and bigger change. Introducing AI there can be dangerous, and that’s a conversation all its own . You hear horror stories about AI initiatives that failed—and what failed them. Agentic frameworks stall because the retrieval layer can’t be trusted.…