AI Engineering: A New Discipline for the Agentic Era

Most AI projects do not fail in the demo. They fail when the demo meets real operations: owners are unclear, costs rise, quality drifts, and rollback is slow. Field evidence has shown this pattern for years in machine-learning systems (Sculley et al., 2015). AI engineering is the discipline that closes that gap by turning model capability into a service that can be run, measured, governed, and improved over time.

To keep this clear and consistent, we use the Software Engineering Institute (SEI) definition as our base: AI engineering combines systems engineering, software engineering, computer science, and human-centered design to create AI systems for real mission outcomes. In practical terms, this paper uses AI engineering to mean running AI in production with clear reliability, safety, and accountability expectations. This framing aligns with the NIST AI Risk Management Framework and systems life-cycle engineering practice, and it is broader than only training or benchmarking a model.