observability

AI is climbing through performance engineering and observability the way autopilots climbed through aviation: eating the mechanical work first, then the analytical work, and now reaching for the interpretive work. This essay proposes a durable reading grid — an autonomy ladder transposed from autonomous driving — to locate any tool, present or future, and to ask the only question that stays stable over time: as the machine climbs each rung, where does the human move, and what must the human still know how to do?

Tool taxonomies organise observability by metrics, traces, logs, and profiles. Practitioners organise it by intent: what am I trying to understand, debug, or prove? This article reframes the observability stack around six common intents — Golden Signals, latency propagation, high-cardinality debugging, low-overhead profiling, black-box, and cost-efficient at scale — with the workflows, the right tool combinations, the anti-patterns to avoid, and a dedicated treatment of how unified APM platforms (Datadog, New Relic, Dynatrace) fit in the intent-routing framing.