Context Is Not All You Need

Agentic data engineering is the move from a data platform that detects problems and hands them to a person, to one that closes them. This page follows a paper about the hard part of that move. Its argument is that a governed context layer is necessary and not sufficient. On the most realistic public benchmark for enterprise data work it knows of, roughly eighty-five per cent of wrong answers are attributed to planning and to implementation and extraction errors, not to failing to find the right table.

• The failures that dominate are failures of doing: roughly forty per cent planning, forty-five per cent implementation and extraction, fifteen per cent schema finding.
• Data agents with write access change the stakes: a planning error in read-only analysis is a wrong number, with write access it is an incident.
• Multi-agent data engineering makes authority definable: twenty narrow specialists, each scoped to the one slice of the estate it is allowed to change.
• Human-in-the-loop agent governance lives in the path a change travels: consequence, blast radius and a named approver are settled before anything runs.