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Your services already know why they broke. You just delete that knowledge at deploy time.

Tobi Lekan Adeosun 2026年07月17日 11:14 3 次阅读 来源:Dev.to

I want to start with a moment most of us have lived through. It's 3 a.m. A dashboard is red. You're eight terminals deep in grep , trying to work out which service actually fell over and why. And the whole time there's this nagging feeling that you're doing archaeology on a system you wrote last month. Here's what got under my skin about it. The answer was never actually lost. Back in the source code it said, in plain terms, that the payment service talks to Postgres through a connection pool. That this retry backs off three times. That this particular dependency is external and you must never, ever try to "just restart it." That was all right there at build time. Then we packaged everything up, deployed, threw that structure in the bin, and asked a sleep-deprived human to reconstruct it from log lines. That gap bugged me enough that I spent a while building something around it. This post is about that. Autoscaling is good at the wrong problem We've gotten genuinely good at reacting to resource pressure. Traffic climbs, a box gets slow, CPU pins, and the autoscaler adds capacity or sheds load. No complaints there, it's kept things running for years. The problem is it has no idea what your app is for . It can't tell a service that's slow because it's healthy and hammered from a service that's fast because it's quietly writing garbage to the database. It never had a model of the application in the first place. So a whole category of failures just sails right past it. A connection pool getting drained by something downstream. A poison message kicking off a retry storm. A schema change that breaks one code path and leaves the other one looking perfectly fine. Infrastructure that only thinks in CPU and memory is blind to all of that. The "throw an LLM at it" era The going answer right now is to bolt a large language model onto your observability stack. Fire hose all the logs, traces, and metrics at a big central model and ask it what happened. I get why. I also think it'

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