Every production system is an experiment. The uncomfortable question is whether anyone is still measuring it.
In theory, most systems begin life with intention. They are reasonably specified, carefully implemented, and aligned to the constraints that exist at the time. In practice, what breaks them is not the absence of documentation or process, but the slow accumulation of unobserved change.
The most costly software degradation I’ve seen does not come from sloppy engineering. Rather, it comes from behavioral drift that goes unmeasured for too long.
The Persistence of Assumptions
Early assumptions tend to stick around far longer than the conditions that justified them.
A performance shortcut made under load.
A dependency added for speed.
An exception introduced “just this once.”
Each change is locally rational. Each is usually defensible in isolation. But systems do not fail locally. They fail in aggregate.
Over time, these small, rational decisions compound into something else entirely: a widening gap between how a system is assumed to behave and how it actually behaves in production.
This gap rarely announces itself. There is no single breaking change. Instead, there is a gradual erosion of predictability. Latencies creep. Edge cases proliferate. Recovery becomes slower and more and more manual. Teams compensate with heroics rather than insight.
Eventually, leadership senses fragility but struggles to articulate why.
Why Specifications Don’t Save You
When this happens, the instinctive response is often procedural:
- Write more detailed specs
- Enforce stricter review gates (CABs, ARBs)
- Improve ticket quality
- Add more documentation
None of these address the root problem.
Specifications describe intent, not reality. They capture what a system is supposed to do, not what it is doing today under real load, real data, and real usage patterns.
Even spec-driven approaches, which I often support, particularly in AI development, are insufficient on their own. They help constrain design, but they do not continuously validate behavior. Drift happens after the spec is written.
This is why “better JIRA tickets” rarely prevent degradation. We are not failing due to unclear intent but because the feedback loop between intent and behavior has decayed.
Drift Is a Measurement Problem
At its core, behavioral drift is not a process problem. It’s a measurement problem.
Most teams lack a living, executable understanding of how their system behaves in production. They rely on dashboards that track symptoms (latency, error rates) but not assumptions. They alert on outages, not on divergence.
As a result, systems evolve silently until something snaps.
This is especially pronounced in fast-moving environments like AI, where iteration is frequent and feedback loops are informal. Models change. Prompts change. Data distributions shift. Infrastructure adapts. Yet the assumptions embedded in code and architecture often remain implicit.
The faster the iteration, the faster unmeasured drift accumulates.
Tests as Executable Hypotheses
This is where a TDD-oriented mindset becomes valuable. Don’t read it as dogma, but as discipline.
I don’t advocate test-driven development as a rigid methodology or a purity exercise. I advocate it as a way to make system behavior explicit.
Well-designed tests act as executable hypotheses about how a system is expected to behave. They encode assumptions in a form that can be continuously evaluated. When they fail, they force a question that matters far more than “how do we fix this?”
Is this failure signaling an intentional evolution of behavior or an unintended regression?
That distinction is where decision quality improves or erodes.
Without it, teams default to patching. They make the test pass again without interrogating whether the behavior should pass. Over time, this reinforces drift instead of arresting it.
With it, failures become signals. They prompt conversations about intent, trade-offs, and changing constraints. They surface architectural pressure early, when it is still cheap to address.
From Heroics to Empiricism
Mature engineering organizations are not defined by the absence of incidents. They are defined by how quickly they detect divergence between belief and reality.
Continuous empirical validation shifts teams away from heroics and toward understanding. It replaces intuition with evidence. It creates a shared, objective reference point for decision-making.
Most importantly, it makes evolution explicit.
Systems are allowed to change, but those changes are observed, measured, and understood.
Closing Thought
Every production system is an experiment, whether we acknowledge it or not. The real risk is not that systems evolve, but that they evolve without feedback.
Specifications define intent.
Process enforces coordination.
Measurement preserves alignment.
When behavior is continuously validated, systems remain legible. When it isn’t, drift fills the void.
And by the time leadership notices, the cost is already high.
andersonnjen.com 