The Limits of Human-Scale Ops
In any large-scale production network, things break all the time.
You know the drill. A BGP neighbor drops unexpectedly. A route that should be tagged isn’t. A link starts flapping at the worst possible moment. Reachability to a critical service gets degraded, and the root cause is buried three hops away in a misconfigured filter that no one’s touched in six months.
If you’ve worked in network operations for more than a few months, you’ve seen this story unfold dozens of times. Maybe hundreds.
And the moment it happens, the same dance begins: a page wakes someone up. Someone triages logs, pulls telemetry, and checks configs. Slack threads fill with packet captures, grep chains, and half-baked theories. Eventually, someone executes the right fix. Maybe it takes 10 minutes. Maybe 60. Maybe more.
Then life goes on, until the next one.
This is the manual response model. It’s how nearly every network team starts: build the infra, monitor it, and rely on human intelligence to put out the fires. And at smaller scales, it works. There's context. There's ownership. There's institutional memory.
But in hyperscale environments, where traffic flows span continents, edge fabrics shift constantly, and every second of downtime has cascading consequences, this model breaks.
Not because the engineers aren’t skilled or because the tooling is bad.
But because you can’t scale human response to machine-speed failure.
The network doesn’t wait for humans to catch up. It fails at wire speed.
And when you’re waking people up to debug the same handful of issues night after night, you’re not just wasting time: you’re burning out your most valuable people. You’re building a culture of fatigue. You’re over-indexing on heroism instead of systems design.
So the question isn’t how fast a human can fix a failure; it’s why a human needs to fix it at all.
That’s the shift. That’s the provocation.
And that’s what this article is about: how we moved from reactive, ticket-driven incident response toward autonomous remediation pipelines, and what we learned in the process.
These systems don’t just speed up resolution. They change the operating model. They let humans do what they’re best at: engineering, not babysitting. And they force you to codify what your team actually knows, instead of rediscovering it every time the pager goes off.
This doesn't concern perfection but progress.
If your network is big enough to fail in repeatable ways, it's big enough to heal itself. Right?
Let’s get into it.