Context decay, orchestration drift, and the rise of silent failures in AI systems

The most expensive AI failure I have seen in enterprise deployments did not produce an error. No alert fired. No dashboard turned red. The system was fully operational, it was just consistently, confidently wrong. That is the reliability gap. And it is the problem most enterprise AI programs are not built to catch. We have spent the last two years getting very good at evaluating models: benchmarks, accuracy scores, red-team exercises, retrieval quality tests. But in production, the model is rare