AI Dictionary › AI Fundamentals
Observability is the ability to understand a system's internal state from the signals it emits. It goes beyond traditional monitoring: monitoring answers predefined questions (is the server up?), observability lets you investigate unforeseen ones (why does this specific user get slow responses only on Tuesdays?).
It rests on three pillars: logs (recorded discrete events), metrics (numeric values aggregated over time, like requests per second) and distributed traces (the journey of a single request through every service it touches). Tools like OpenTelemetry, Prometheus and Jaeger collect and correlate these signals, making it possible to reconstruct what actually happened.
In AI, observability has become a discipline of its own: dedicated platforms trace prompts, responses, token costs, latencies and answer quality for LLMs, while for agents they trace decision chains and tool calls. It is the technical foundation of AI governance: you can only govern what you can observe. Beyond AI, it is standard practice in any microservices architecture.
The term comes from control theory: the Hungarian mathematician Rudolf Kalman formalized it in 1960, defining a system as observable when its internal state can be inferred from its outputs. Software engineering adopted it in the 2010s, driven by the infrastructure teams of large web platforms and by figures like Charity Majors, co-founder of Honeycomb, who popularized its distinction from plain monitoring.
Grace's 7 evaluation dimensions are a form of observability applied to your prompts: measurable signals that reveal the state of your skills and where to intervene. The daily gym's 0-100 score works as a metric that tracks your evolution over time.
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