Agentic engineering is the practice of building systems where AI agents plan and act across multiple steps, using specifications, gates, and evaluation loops to keep autonomous behavior under control. It applies engineering discipline, version control, testing, and verification, to software that makes its own decisions.
These controls govern one kind of drift: the code and the process. They do far less for another kind. The same pipeline with the same prompts can produce a different result next month because the input data shifted, even when nothing in the code changed.
A model rarely fails the way a verification loop is built to catch. It passes the demo and breaks weeks later when the data state it runs on is no longer the state it was validated against. Code can be locked once; the data state has to be reproducible on demand.