For engineers and teams shipping LLM products: curious what your real-world monitoring setup looks like vs. what it actually catches. The common stack I see: latency tracking, token cost monitoring, basic error rates, maybe LLM-as-a-judge scoring. What I hear less about: what slips through. A few patterns I keep running into: - Outputs that score well on evals but fail on nuanced downstream tasks - Failures that only surface after users stop engaging (silent failures vs. flagged ones) ...