AI-Native Observability Patterns and Anti-Patterns

AI-native observability requires teams to make both production systems and AI-driven workflows explainable through connected, verifiable telemetry. This Refcard explores patterns for preserving trace context, standardizing telemetry with OpenTelemetry, grounding AI-assisted investigations in evidence, and instrumenting agentic workflows end to end. You’ll also learn how common anti-patterns — including aggressive sampling, semantic noise, unsupported AI summaries, and unobservable agent execution — can weaken incident analysis and make automated findings harder to trust.