Software is the only kind of machine we build that we can’t see. Bridges bend, engines vibrate, circuits overheat — their state is visible. Software hides its behavior until it fails completely.
Observability gives it form. It turns invisible execution into visible behavior, exposing what’s really happening between “works” and “broken.” Without it, we operate on belief, not knowledge.
Airplanes show why this matters. They rarely fall out of the sky without warning. Long before disaster, the cockpit lights up: cabin pressure drops, fuel levels fall, temperature rises. A pilot’s job isn’t just to fly, but to see — to interpret the constant stream of feedback and act before drift becomes failure.
That’s exactly what observability does for software. Metrics, logs, and traces are our instruments. They show how a living system behaves while it’s still alive. A service that only reports “up” or “down” is like an aircraft with two lights: takeoff and impact.
Those instruments do more than inform the pilot. They close the feedback loop. The aircraft regulates itself through thousands of tiny automated corrections that keep it stable in an unpredictable world.
Control theory has a name for this difference. An open-loop system acts once and hopes its model is correct. A closed-loop system measures, compares, and corrects continuously. Every aircraft, every thermostat, every industrial controller is closed loop. They know the world isn’t perfect, so they watch it, measure it, and compensate.
Unobservable software, by contrast, is open loop. It assumes its model of reality still holds and discovers otherwise only when something breaks. Observable software accepts uncertainty. It becomes a closed loop — measuring, learning, and correcting in real time.
An unobserved system is an unknown system. And an unknown system can’t be trusted, extended, or relied upon — only feared. An unknown system requires hope, and hope has no place in engineering or in business.
A well-observed system, however, is the opposite: it’s something you can rely on. When you can see how a system behaves, you can extend it with confidence and build on it without anxiety. Observability turns unstable ground into a stable pillar — a part of the organization that supports change instead of resisting it.
All modern automation is built on closed loops. Kubernetes reconciles desired and actual state. Terraform detects drift. Autoscalers adjust capacity by reading metrics. Without visibility, none of these systems could act intelligently; they would just push changes and pray.
The same logic holds above the technical layer. Businesses are control systems too. They rely on telemetry — KPIs, forecasts, dashboards — to steer and correct course. No executive accepts “we’re doing fine-ish.” Decisions demand feedback. System health deserves the same discipline.
Automation depends on visibility, but so do the humans running it. Some organizations still rely on people watching dashboards around the clock, waiting for something to break. That’s not observability — that’s manual labor disguised as awareness. In an aircraft, warning lights and alarms mark when a parameter crosses a limit — they are the bridge between observation and action. Software needs the same. If you can see it, the system should tell you when it drifts. Alerts are how observability closes the loop for humans: they translate measurement into attention. They wake us up when the world starts to diverge from expectation.
Without alerts, observability is passive. With them, it becomes active — a system that not only sees itself but communicates when it’s in danger.
Observability doesn’t stop at infrastructure; it’s how the entire organization learns to see itself. When every team operates blind to the effects of its changes, the whole business becomes an open loop — busy, reactive, and surprised by outcomes. True observability connects technical and human systems: engineering notices business drift; business notices technical drag. Both share one reality instead of two stories.
You can’t steer what you can’t see. And you can’t lead through assumptions any more than you can fly an aircraft through clouds without instruments. Observability isn’t about data; it’s about agency — the ability to act with confidence instead of hope.
Visibility is what turns design into engineering. Without feedback, you don’t operate a system — you just hope it keeps flying. Observability doesn’t make software perfect. It makes it knowable — and only what’s knowable can be trusted.