Every engineering team runs into the same monitoring trap. It doesn't matter whether you're a platform organization at a public company or three product engineers sharing a pager; the shape is identical, and until now, nobody has escaped it.
The loop every team knows
Stage one: you ship. Monitoring almost never makes it into v1 of anything. You're focused on getting the feature out, and observability is the thing you'll add "once it's stable." Then the product breaks, and you find out because a customer escalated. There's no worse way to learn about an outage than from the person paying you.
Stage two: never again. The natural reaction is to monitor everything. You instrument every service, every queue, every database, every edge case anyone can imagine going wrong. It feels great. You're covered. No customer will ever need to tell you about your own incident again.
Stage three: too much. Then a minor issue fires two hundred alerts at once. Then it happens again next week. The on-call channel becomes a firehose, and your team develops the only rational response to a firehose: they stop drinking from it. Alerts get muted, snoozed, and routed to channels nobody reads. Functionally, you're back at stage one. You have monitoring, but nobody is listening to it.
Stage four: too little. So you get disciplined. You read the Google SRE book, adopt SLOs on critical user journeys, and prune everything else. Only page on what users actually feel. The noise drops, the on-call rotation stops burning people out, and you feel like a real engineering organization. You are now monitoring too little.
Stage five: the big one. The next major incident arrives, and your SLO monitor catches it, but too late. The database had been running hot for an hour. The leading indicators were all there; you had deliberately chosen not to watch them, because watching them was what buried you in stage three. The postmortem asks the inevitable question, "how could we have detected this sooner?", and produces the inevitable action item: add more monitoring to the lower-level systems. Welcome back to stage three.
From here it's a permanent oscillation between too much and too little, with a side quest that makes it worse: threshold tuning. Every monitor needs a level, and the rules are static and non-judgmental. CPU above 80% for five minutes, page someone. So engineers spend years nudging thresholds up after false alarms and down after misses, hunting for a "perfect threshold" that does not exist, because the right answer depends on context a static rule can't see.
None of this is a failure of discipline. The loop exists because every stage is rationing the same scarce resource: human attention. You can't watch everything, so you must choose what to ignore, and whatever you ignore is where the next incident comes from.
The rule that made the compromise official
The industry's answer to this loop was written down a decade ago in Google's SRE book: page on symptoms, not causes. If users aren't affected, don't wake anyone up.
It's worth being precise about what aged and what didn't. The book's philosophy of SLOs, error budgets, and the golden signals has held up remarkably well. But the specific rule about monitoring only at the user-impacting level was never a claim about the ideal way to run software. It was a concession to biology. A human was going to read every page, and humans are finite, so the rule's real content is this: accept blind spots, because the alternative is destroying your team. That trade made sense in a world where the pager always ended at a person. That one page of the book is due for coaster duty, not because the thinking was wrong, but because the constraint it was designed around no longer holds.
The bottleneck was never data
Here's the thing: you already have visibility into everything. Your systems emit enormous volumes of metrics, logs, and traces, and you ship all of it to an observability platform. And you pay handsomely for the privilege. The data describing your next incident, an hour before it happens, is almost certainly already sitting in a dashboard nobody is looking at.
Data was never the constraint. Consumption was. The entire discipline of monitoring, with its sparse alerts, careful thresholds, and SLO-only paging, is a set of techniques for compressing infinite data down to what one distracted human can absorb at 3 a.m.
That constraint just disappeared. The entity consuming and triaging your operational data no longer has to be a human. You can hire an AI engineer whose attention doesn't fatigue, doesn't get alert blindness, and reads the two-hundredth alert with exactly the same care as the first. Human capacity is no longer the ceiling on your observability.
Detect everything. Act on what matters.
Once the bottleneck is gone, the optimal strategy inverts. Monitor aggressively. Lower your thresholds so signals fire earlier. Push monitors deep into the stack, past the service level, down to individual functions if you want. And before you flinch at the observability bill: platforms charge for the data you ingest, not for how many monitors evaluate it, and this is data you're already ingesting. Alert volume will skyrocket, and that is exactly the point. Think of it as continuous bloodwork: you don't want fewer tests, you want comprehensive tests read by someone qualified, so problems surface while they're still cheap to fix.
This also ends the threshold treadmill. When the rules are static, the threshold has to embody all the judgment, which is why you could never get it right. When an AI engineer applies judgment downstream, thresholds just need to be sensitive. It knows the difference between a single-instance CPU spike that recovers in thirty seconds and a fleet-wide CPU climb that signals an autoscaling stock-out and will take you down in twenty minutes. Same metric, same threshold, completely different response.
And "response" is the right word, because this isn't a smarter notification layer. A notification is where a monitoring system's job ends; it's where an AI engineer's job begins. It prioritizes what actually matters, investigates the cause, identifies the fix, and either acts within the guardrails you define or escalates to a human with the full picture already assembled: not a page that says something is wrong, but a finding that says here is what's wrong, here is why, here's what already been applied, and here's what to do next.
And none of it is a black box. Every conclusion traces back to the specific alerts, logs, and metrics that produced it, so you can audit the reasoning instead of trusting it. If an issue can't be resolved within a window you configure, it escalates to a human. Deterministically, every time.
What this looks like in practice
This is what we built Bacca to do: an AI engineer that owns frontline on-call. In a recent incident with one of our clients, an outage fired roughly two hundred alerts within an hour, spanning the database layer, microservices, and user-facing symptoms, all downstream of one root cause. No human can triage that in real time; a whole team would lose hours to it. Bacca read every alert, correlated them across layers, and collapsed the storm into one escalation that actually mattered. It identified the specific database that was overloading, the newly deployed job that triggered it, and the concrete steps to fix it. Two hundred signals in, one diagnosis out.
What this changes for your team
Everyone has always wanted to be proactive. Noise made it irrational. Now that the barrier is gone, what you get depends on your seat.
If you lead platform or infrastructure engineering, this is the first genuinely proactive operational stance that doesn't tax your team. You can watch every leading indicator, catch cascading failures while they're still local, and raise reliability without paying for it in alert fatigue and on-call attrition.
If you're a product engineer, you don't care about monitoring posture, and you shouldn't have to. What you get is simpler: the operating half of your job disappears. No triaging, no threshold tuning, no 3 a.m. archaeology through dashboards. You build software; something else operates it.
Stop playing defense
There's a bigger conversation happening about the future of software: a world where humans architect and specify, and no human writes or operates the code. Bacca sits at the front of that conversation, and we think that world is coming.
But you don't need the future to act. The data is already flowing, you're already paying for it, and the human limitation that forced you to ignore most of it is gone. The too-much/too-little loop was never a law of nature; it was a symptom of a constraint that no longer exists.
Detect everything. Act on what matters. See it live: book a demo at Bacca.ai.
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