When a system fails, our first instinct is to look for a defect. We search for the faulty line of code, the missing configuration, the human error that can be isolated, corrected, and prevented from happening again. This framing is attractive because it promises a clear cause and a contained solution.
In practice, however, most real-world failures do not originate from broken components. They emerge from misalignment between otherwise functioning parts. The system fails not because something is incorrect in isolation, but because assumptions diverge at the boundaries.
Bugs are local; failures are systemic
A bug is a local property. It exists in a specific function, service, or configuration and usually behaves deterministically once triggered. When identified, it can often be fixed without changing the surrounding structure.
Failures are different. They are systemic events that arise when multiple components interact in ways that were never fully coordinated. Each part behaves “correctly” according to its own rules, yet the system as a whole produces an undesirable outcome. This distinction matters, because treating systemic failures as local defects leads to fixes that feel satisfying but fail to prevent recurrence.
Failures almost always surface at interfaces, not inside components.
A familiar coordination pattern
Consider a system composed of two services that are both behaving as designed. One service assumes that a request should complete within two seconds and treats anything slower as a failure. The other service has no such constraint; it occasionally performs heavier work and returns successfully after five seconds. No bugs exist. Both services meet their internal expectations.
In production, this mismatch manifests as intermittent failures. Requests are retried, load increases, and timeouts appear without obvious errors. Each team investigates its own service and finds nothing wrong. Metrics look normal in isolation. Logs contain no clear signal.
The failure does not live in code, infrastructure, or configuration. It exists entirely in the gap between assumptions. No explicit contract defined acceptable latency. No shared signal communicated intent. No feedback loop surfaced the mismatch before it escalated.
This pattern repeats endlessly in different forms: alerts without clear ownership, manual steps that are “obvious” to one team and invisible to another, APIs whose error semantics are interpreted differently by consumers, escalation paths that exist socially but not structurally. In every case, the system fails because coordination was never fully designed.
Communication is an architectural concern
In distributed systems, engineers assume communication is unreliable by default. Messages can be delayed, reordered, duplicated, or lost, so systems are designed with timeouts, retries, idempotency, and circuit breakers. These mechanisms exist because communication cannot be trusted to behave perfectly.
Human systems are no different, yet we often treat them as if they were. We rely on informal understanding, tribal knowledge, and implied responsibility. We assume others know what we meant, understand the urgency of an alert, or recognize when something has crossed a critical threshold.
This asymmetry is a major source of failure. We design machine-to-machine communication defensively while treating human-to-human and human-to-system communication optimistically. The result is predictable: coordination breaks under stress.
Failure as loss of shared reality
Most failures are not sudden; they are the result of slow divergence. Teams drift apart in understanding. Systems evolve independently. Signals lose meaning. What one part of the system believes to be true no longer matches reality elsewhere.
By the time failure becomes visible, it feels abrupt only because the warning signs were never shared in a way that could be acted upon. The system did not lack intelligence; it lacked a shared language for reality.
Seen this way, failure is less about malfunction and more about loss of coherence.
Observability as communication, not tooling
Observability is often framed as a technical capability, but its deeper purpose is communicative. Logs, metrics, and traces are not merely debugging tools; they are how a system explains itself to the people responsible for it.
A system that fails silently, emits ambiguous signals, or overwhelms operators with noise is not just poorly instrumented. It is poorly communicative. Operators are forced to infer state, guess intent, and make decisions with incomplete information.
The same principle applies to organizations. Teams without clear feedback loops drift out of alignment. The absence of visible failure is mistaken for health, until accumulated misalignment surfaces as crisis. Reliability, in both technical and human systems, is not the absence of error but the early detection of drift.
Contracts over assumptions
High-reliability systems depend on explicit contracts. These contracts define not only success paths, but also failure modes, timing expectations, and responsibility boundaries. They reduce ambiguity at interfaces and minimize the need for interpretation.
Where assumptions replace contracts, fragility follows. An API is expected to behave “reasonably.” A team is expected to “handle it.” An alert is expected to be “obvious.” None of these expectations scale.
This does not imply bureaucracy or excessive process. It implies intentional design at boundaries. Every interface, whether technical or organizational, should make expectations explicit enough that coordination does not rely on memory, goodwill, or shared history.
Error handling and blame handling
How a system handles error reveals its maturity. Robust systems surface failure clearly and recover gracefully. Fragile systems obscure failure until it becomes catastrophic.
Organizations behave the same way. When coordination fails, immature environments search for blame. Mature ones search for signal. They ask what information was missing, what assumptions were unexamined, and which interfaces failed to communicate reality in time.
Blame suppresses information. Signal strengthens systems.
Designing for coordination, not perfection
The goal of reliability is not to eliminate errors, but to preserve coherence when errors occur. That coherence depends on communication.
Every boundary in a system should answer three questions clearly: what is expected, how deviation will be detected, and who is responsible for recovery. When these questions are unanswered, failure becomes inevitable.
Once you start viewing failures as coordination breakdowns rather than defects, many past incidents suddenly make sense. The bug was never the real problem. The system simply had no shared way to understand what was happening.
Reliability is built not by fixing every mistake, but by designing systems—technical and human—that can communicate clearly when things go wrong.
This is also why the most reliable systems often feel uneventful to operate. Their lack of drama is not accidental; it’s the result of coordination designed into every boundary. I previously wrote about this outcome in Good Systems Are Boring – and That’s Their Beauty.