There’s a script in every engineering organization that everyone is afraid to touch. It’s a relic of a bygone era, often born from the early promises of DevOps and SRE to eliminate manual work. It’s usually old, written in a language no one on the current team is fluent in, and it handles a process that is both critical and poorly understood. It just works, silently, in the background. It’s a masterpiece of automation, and also a time bomb.
When it inevitably breaks, a special kind of panic sets in. The person who wrote it left the company years ago. There’s no documentation explaining its logic. The team has to drop everything to reverse-engineer a process they’ve been successfully ignoring for years. They are learning how the sausage is made at the worst possible moment: while the factory is on fire.
This is the paradox of automation. We build it to make our systems more reliable and our lives easier. We abstract away complexity and toil. But in doing so, we often automate away our own awareness. We trade active knowledge for passive success. And that trade has a hidden, long-term cost.
The problem isn't the automation itself. It's the way it can create a gap between an engineer and the system they are responsible for. When you perform a task manually, you build up a rich, tacit understanding of it. You know the weird edge cases. You know which steps are slow, which are brittle, and which require careful timing. You have a feel for the rhythm of the work. This knowledge is hard to write down, but it’s what allows you to debug effectively when something goes wrong.
A good script captures the happy path of that manual process. A great script might even handle a few of the known failure modes. But almost no script captures the full context, the why behind each step. It encodes the instructions, but not the experience. And over time, as the team that held that experience turns over, the collective memory fades. The organization is left with only the artifact - the script.
We see this pattern everywhere in modern software delivery. A junior engineer joins a team with a mature CI/CD pipeline. They push a commit, the pipeline runs, and a container gets deployed to production. It feels like magic. But when a deployment fails, they don’t know where to start. Is it a problem with the build? The test environment? The container registry? The container orchestration layer? The pipeline is a black box, a series of green checkmarks that have suddenly turned into a red X. Their job becomes not to understand the system, but to placate the machine. They start poking it, re-running jobs, hoping to find the magic incantation that makes it green again.
This is a fragile state. The system’s robustness has been outsourced to a machine, but the team’s ability to recover from the machine’s failure has atrophied. It’s like a pilot who only ever flies on autopilot. They’re perfectly capable as long as the systems are nominal. But when the autopilot fails in bad weather, they need the raw stick-and-rudder skills they haven't practiced in years. The muscle memory is gone.
This isn’t an argument against automation. It’s an argument for a different kind of automation - one that aims to augment awareness, not replace it.
What does that look like? It might be automation that is more transparent and observable. Instead of a single "deploy" button that hides twenty steps, it could be a tool that visualizes those steps and explains what’s happening. The output of the script shouldn’t just be SUCCESS or FAILURE; it should be a narrative of the work it just performed. It should tell you what it did, why it did it, and what was unusual about this particular run, improving the team's overall observability.
It also means changing how we treat our automated systems. We need to treat them as living documents, not just as tools. The script that provisions a new service, our Infrastructure as Code (IaC) isn’t just code; it’s the most up-to-date specification of how that service is built. When we need to understand the service, our first instinct should be to read the IaC, not to poke around in the cloud console. The automation should be the source of truth.
And perhaps most importantly, we need to find ways to deliberately practice. SREs call this "game days" - intentionally breaking things in a controlled way to see if you can fix them. It’s the engineering equivalent of a fire drill. You run the manual process not because the automation has failed, but precisely because it hasn't. You do it to refresh the team’s knowledge, to find the gaps in your understanding, and to build the confidence that you can handle a real failure.
Every piece of automation we write is a contract with our future selves. We are betting that the time we save will be greater than the cost of the awareness we lose. But we need to read the fine print. If we’re not careful, we can automate ourselves into a corner, leaving the next generation of engineers to operate systems they don’t truly understand, waiting for the one day when the magic stops working.
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