AI systems in production are fundamentally opaque — even their creators can't fully explain individual outputs. The solution isn't fixing the model but building observability infrastructure around it, similar to how aviation built trust through black boxes and audits rather than making planes understandable to passengers. Four instrumentation layers are proposed: (1) development-layer telemetry for coding agents like Claude Code and Codex, tracking token usage and acceptance rates; (2) operational metrics covering spend, token limits, and user behavior; (3) decision-path tracing using OpenTelemetry spans to reconstruct why an agent took specific actions; and (4) quality evaluation with hallucination detection, toxicity scoring, and real-time guardrails for PII and prompt injection. OpenTelemetry's GenAI semantic conventions provide the standard foundation, and Coralogix's AI Center is presented as the backend that ties all four layers together.
