Make Sure say People Trust AIOps: Di Need for Meta-Observability

As companies dey use AIOps for automated alert plus root-cause analysis, dem dey face new wahala: to put observability for AI tools mean say e go reduce how dem dey see how those tools dey make decision. Observability platforms dem be complex system with metrics, logs, traces plus AI models fit get wahala like data pipeline errors, model drift, bias and infrastructure palava. If no get "meta-observability," teams no fit detect dropped logs, configuration drift or AI-blind spots wey fit spoil trust for automated workflows. To solve this, make you treat your observability stack as critical service: dey watch platform latency, ingestion rates, API errors and AI-specific metrics like token latency. Put data quality checks, standardized instrumentation (like OpenTelemetry) and model explainability reports wey talk about accuracy and false positives. By applying traditional monitoring principles to AI-driven observability, engineers fit keep reliability and stop hidden failure for AIOps solutions.