When AI tools move from a test environment to real-world use, the first “surprise” a developer encounters is rarely about accuracy. It’s usually something more problematic: the system behaves inconsistently, costs climb faster than expected, and the same job seems to run multiple times.
That’s not an AI problem. That’s a distributed systems problem. And in AI systems, this particular failure is extra problematic because every duplicate run has a direct dollar value impact. Idempotency is the fix. Not the only fix, but often the most impactful one.