Effective AI observability is a telemetry problem, not a tool problem.

An LLM application can return HTTP 200 at 95ms with every dashboard showing green, while the model confidently returns wrong answers to 23% of queries in a week with no one knowing. The post argues that external systems appear functional while internal AI behavior stays broken and invisible to standard monitoring, making AI observability a telemetry problem rather than a tooling one.