Still haven't found a tool that can accurately detect when an LLM starts degrading in quality. The challenge is that model performance decay isn't just about benchmarks dropping - it's about subtle shifts in reasoning coherence, instruction following precision, and output consistency that standard eval suites miss. We need something that tracks drift in real production scenarios, not just synthetic test sets. The irony is we're building increasingly complex models but lack the instrumentation to monitor their actual cognitive stability over time.