Troubleshooting: The Survival Skill We Forgot We Needed

Troubleshooting isn’t a bonus skill in tech. It’s survival.

I keep telling people this, not because it’s fashionable advice, but because it’s been reality for me ever since my early days. When I was preparing for the RHCE (Red Hat Certified Engineer) certification (back in the 2004–2010 era) one of our favorite exercises was playing with the RedHat’s troubleshooting scripts. If you’ve ever touched RHCE training from that time, you know what I’m talking about: a script would inject random problems into a perfectly good system, and your job was simple — fix it. No hints, no handholding, no shortcuts. Just you, the logs, and your brain.

It was brutal. And it was brilliant.

The certification itself was 100% practical. No theory exams. No multiple-choice salvation. If you couldn’t troubleshoot your way through a broken system, you couldn’t pass. Looking back, I think that early exposure shaped my instincts more than anything else. It taught me something fundamental: Everything is solvable if you stay calm and understand what’s actually going wrong.

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Fast forward to today.

We live in a world where if something doesn’t work, the instinct isn’t to troubleshoot — it’s to rebuild. Just destroy and redeploy. Docker container acting weird? Rebuild it. Vagrant VM misbehaving? Recreate it. Kubernetes pod unhappy? Restart the pod. If all else fails, switch to a different base image, rebuild everything, and hope the gremlins are left behind. Rinse, repeat, and pray you don’t end up debugging the same ghost all over again.

And while rebuilding works sometimes, it doesn’t teach you anything. It just delays the moment the same issue resurfaces at 3 AM — and this time, no amount of “docker restart” will save you.

Troubleshooting means understanding. And understanding means taking responsibility. If you think you’ve found the cause of a problem, it’s your duty to replicate it, to validate it, and to make sure that if it ever happens again, your system can handle it.

Anything less isn’t troubleshooting — it’s wishful thinking.

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Yesterday, a friend called me for help.

That’s not unusual — people often reach out when things go wrong. But this time it hit a little different.

He was working on a project and, as is tradition, had googled his way into an “all-in-one” solution that claimed to solve everything : setup, deployment, orchestration, maybe even dinner plans if you squint at the marketing. It “mostly worked,” he said, except for the part that didn’t. And unfortunately, the part that didn’t was the one he needed.

He’d followed the documentation, deployed the stack, and everything spun up beautifully. But then came the blank stares. The logs were cryptic, the error messages unhelpful, and the shiny abstraction that promised simplicity turned into a fog machine.

So we jumped on a call.

And like any good impromptu therapy session, we began with denial, moved through confusion, and eventually hit that magical stage called “Oh wait… that’s all it was?”

It turned out the problem wasn’t that complex. But it required peeling back a few layers, reading documentation beyond the first screen, and — most importantly — being okay with not understanding things right away. We poked around until the fog cleared. And somewhere along the way, he had a realization: “It’s not that these things are hard. I just panic when I don’t know how they work.”

That’s the bit that stuck with me.

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We live in an age of wonderful abstractions. Tools have gotten so good at making us productive that they’ve also gotten great at hiding complexity. That’s fine — until the moment something doesn’t work, and you have no clue which layer is misbehaving.

It’s like driving a Tesla and suddenly being asked to fix the regenerative braking system with a spanner and YouTube videos. Technically possible, theoretically manageable, but good luck figuring out where the bolts are.

The real danger with all-in-one tools isn’t that they exist. It’s the illusion they sell: that understanding is optional.

Spoiler: it’s not.

You don’t need to know everything. But you do need to know enough — and more importantly, you need the confidence that you can figure out the rest when it breaks. That’s the debugging muscle nobody talks about. And building it doesn’t come from memorizing error codes. It comes from knowing how to read fast, type faster, search smarter, and not spiral into doom when nothing makes sense.

(I wrote about this survival kit here if you want the gory details.)

If I had to summarize the lesson from this whole episode, it’d be this:

Abstractions are like rental cars. Great to use. But if you’re going off-road, you better know what’s under the hood — or have a friend on speed dial who does.

We worked through it patiently, peeled back a few layers, and fixed the issue. Another reminder that with enough persistence, most problems reveal themselves.

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In the infosec world, troubleshooting isn’t just a nice-to-have “it’s a survival skill”. We live surrounded by layers of abstraction, and that’s necessary; nobody can build everything from scratch. But when those layers crack, it’s your ability to dig, understand, and adapt that keeps things running.

Staying calm when things go wrong isn’t magic either. It’s a muscle built over time, by facing enough broken things that you stop fearing the unknown. It’s the same pattern described through the character Brent in The Phoenix Project by Gene Kim and others. When troubleshooting becomes a rare skill, teams end up depending on a handful of individuals who can “make the broken machine work.” And when those individuals are overloaded or absent, everything grinds to a halt. This is also why it’s important not to become a permanent single point of failure. I’ve explored that idea in more detail in From SPOF to Linchpin: How to Grow Without Getting Stuck. Troubleshooting isn’t just about personal survival — it’s about building resilient systems and resilient teams.

But building troubleshooting muscle isn’t the end goal either. As we grow and strengthen these skills, it’s critical to push those learnings into automation. If you find a recurring issue, it should become a checklist, a script, or a system improvement — not another fire you fight manually every week. Organizations that survive the longest aren’t just the ones who troubleshoot well; they are the ones who automate yesterday’s firefights, freeing their engineers to tackle tomorrow’s unknowns.

And in this new era of AI-driven assistance, the stakes are even higher. AI can give you answers — or at least text that looks and feels like an answer. But it’s your core skills, your intuition, and your experience that help you pick the right answer from the noise. Core abilities like reading fast, thinking clearly, and processing abstract connections are what turn AI into a force multiplier instead of a confusion amplifier. Using AI (or any new technology) effectively isn’t about blindly trusting outputs. It’s about standing on a strong foundation and leveraging those tools to soar higher, faster.

Troubleshooting skills are a lost art. Nobody needs them — until they’re the only thing that matters.

Experience doesn’t just fix today’s problem. It makes sure tomorrow’s problem is smaller.