Traditional software is built for deterministic outputs. AI systems are probabilistic, variable, and sometimes uncertain.
The shift is clear:
Testing → Continuous evaluation
Defensive programming → Grounding and guardrails
Binary correctness → Calibrated confidence
One-time QA → Continuous observability and tracing
Reliable AI is not about eliminating uncertainty. It is about designing systems that can measure, manage, and respond to it.