Execution Drift in AI Systems (and Why It Matters More Than You Think)

AI systems are often assumed to be stable. If the code does not change, the system should behave the same way. In practice, that assumption breaks down quickly. Two executions with the same inputs can produce different results. This is not always a bug. It is a property of modern AI systems. Definition: Execution Drift Execution drift is the phenomenon where identical inputs produce different outputs over time due to changes in environment, dependencies, models, or execution conditions. It is one of the most under-discussed challenges in AI systems today. Why Execution Drift Happens Even when a system appears unchanged, several factors can cause outputs to shift: dependency updates runtime version differences model updates or fine-tuning prompt or orchestration changes environment configuration differences non deterministic execution paths These changes are often subtle and may not be visible in logs. But they affect results. A Simple Example A workflow runs today and produces a result