Failure is inevitable. If you don’t go looking for it first, it will find you later, usually at the worst possible moment.
When most developers say “the system failed,” they usually mean something crashed or behaved incorrectly. A process exited. A service returned a 500. A deploy went sideways. While this is true, that definition has always felt incomplete to me.
To me, failure is not the absence of correctness. Failure is a system revealing whether its assumptions were valid under stress. A system that behaves deterministically when things go wrong is not failing. Proper failure is when a system is doing exactly what it was designed to do.
The uncomfortable truth is that if you haven’t designed for failure deliberately, you’ve already accepted silent failure by default. The consequences don’t disappear, they just show up later, when they’re harder to understand and harder to undo.
Failure doesn’t start with correctness
When systems degrade, they rarely do so in the order we expect.
In my experience, the first thing to go is not correctness. It’s understanding.
Happy-path-first design is seductive because the happy path is easy to reason about. It’s linear. It’s testable. It makes demos look clean. But design doesn’t live only in the happy path. Design lives everywhere the system can go.
Once understanding erodes, availability is next. Dependencies slow down. Networks behave unpredictably. Backpressure appears where it wasn’t anticipated. Timeouts and retries interact in ways that weren’t modeled.
Correctness usually fails last, and when it does, it’s often quietly. Data that looks valid but isn’t. State that drifts slowly. Records that are “mostly right” but impossible to reconcile later.
This is why silent failure is so dangerous. Silent failures are where threats to data integrity live, and data integrity is one of the hardest things to recover once it’s been compromised. You can restart a service. You can redeploy infrastructure. You cannot easily unwind corrupted intent.
This is one of the reasons I’ve always appreciated how Go forces engineers to confront error handling directly. if err != nil isn’t boilerplate. It’s a reminder that errors deserve attention. Loud errors create pressure to make decisions. Silent ones create debt that compounds invisibly.
Systems should fail on purpose
A system should not proceed unsafely. When it cannot continue deterministically, it should stop, degrade, or hand control to the user in a way that preserves intent.
That idea didn’t come from software for me. It came from waiting tables.
In my early twenties, I worked at a restaurant in a large mall. During the holidays, it was almost guaranteed that the credit card processing service would go down at least once during peak hours. The computers would still be running. Orders would still be flowing. But payments couldn’t be authorized.
The restaurant didn’t pretend the failure wouldn’t happen. We had prepacked kits next to every terminal. Manual credit card sliders. Carbon paper. Clear instructions. The business didn’t stop operating when the system failed because the failure path had been designed intentionally.
No one assumed the computers would always work. The business assumed they wouldn’t.
That experience stuck with me. Failure wasn’t treated as an anomaly. It was treated as a known state with a plan.
In software, we often do the opposite. We design elaborate automation paths and then act surprised when they encounter situations they were never meant to handle. We let systems continue operating in ambiguous states because stopping feels worse than proceeding. That’s how users end up believing messages were sent when they weren’t, or transactions completed when they failed.
Letting users proceed incorrectly is almost always worse than blocking them. A chat app can ask a user to retry. A bank should never imply funds were transferred when they weren’t. Preserving intent matters more than preserving motion.
Optimizing for the wrong constraints
Some of the most instructive failures in engineering history didn’t come from sloppy design. They came from overconfidence in a narrow set of constraints.
The Tacoma Narrows Bridge is a perfect example. The designers optimized aggressively against the prevailing belief that heavier meant stronger. The bridge was elegant. Efficient. Robust against the forces they were modeling.
What they didn’t model was aerodynamic instability.
By focusing on static loads and direct wind pressure, they unintentionally built a structure that behaved like an airplane wing. Small oscillations created lift. Lift created more oscillation. The feedback loop tore the bridge apart, sending it collapsing in pieces nearly two hundred feet into Puget Sound.
The design was “robust” within its assumptions. The failure came from what wasn’t considered.
This trap rears it’s ugly head in distributed systems all the time. Retry logic that makes perfect sense locally becomes destructive when multiplied across services. Backoff policies that look reasonable in isolation turn into self-inflicted denial-of-service events under load. These aren’t bugs in any one component. They’re emergent behavior caused by incomplete constraint modeling.
Failure is rarely a single mistake. It’s usually the system doing exactly what it was told to do, just not what was intended.
Allowing some failures on purpose
Not all failures deserve equal treatment.
In many systems, especially data-intensive ones, some components must be allowed to fail silently so that more critical guarantees can be preserved. Feature-oriented services can drop requests. Observability pipelines can lag. Notification systems can go dark temporarily.
What cannot fail quietly is the integrity of core data paths.
I’m currently working on a proxy system that routes database connections while enforcing PII masking. Alongside it is a machine learning service that detects abnormal access patterns. If the ML service fails, it cannot be allowed to affect the correctness or safety of the core data path. Its failure should reduce visibility, not compromise integrity.
This kind of prioritization doesn’t happen by accident. It requires deciding up front which parts of the system deserve strict guarantees and which parts exist to enhance, not enforce, safety.
Allowing some failures is not negligence. It’s architectural honesty.
What failing well looks like
A system that fails well does a few things consistently:
- It fails loudly enough that users can understand what happened.
- It preserves intent, even when automation stops.
- It degrades deterministically instead of behaving unpredictably.
- It guides users toward safe recovery instead of pretending nothing happened.
- It remains calm under pressure.
Some of the most validating moments of my career came not from systems that never failed, but from systems that failed exactly the way they were designed to. Automation halted. Users were guided through a fallback. Compliance was preserved. The business continued operating.
I wasn’t available at the time, and that was the point.
Success is not correctness under ideal conditions. Success is preserving meaning when assumptions stop holding and abstractions start to crack.
Failure isn’t a defect in a product. It’s a feature you either design deliberately, or inherit accidentally.