The hardest bugs are not the ones that happen constantly.
The hardest ones are:
- once every few days
- under unknown conditions
- with no obvious pattern Especially in systems that run continuously.
Because by the time you notice the issue, the original state is already gone.
Early on, I used to approach these bugs the wrong way.
I would immediately start reading logs and trying to reproduce the issue locally.
Most of the time, that went nowhere.
Because these problems usually depend on:
- timing
- retries
- load
- specific data states
- interactions between systems
Things that almost never exist in your local environment the same way.
What changed for me was realizing:
The goal is not “find the bug immediately.”
The goal is:
make the system observable enough that the bug exposes itself next time.
So instead of guessing, we start adding visibility around the problem.
Things like:
- tracking state transitions
- storing retry history
- recording execution timing
- correlating events across systems
Not permanent debugging noise.
Just enough context to reconstruct what actually happened later.
Another thing I learned:
Rare bugs are often not random.
They usually happen when multiple small conditions align:
- a delayed queue
- a retry arriving late
- stale data
- another service slowing down
Individually, nothing breaks.
Together, something weird appears for 30 seconds and disappears again.
One mistake I made a lot before:
Trying to “fix” the issue too early.
When you don’t fully understand intermittent bugs, quick fixes usually just hide the symptom temporarily.
So now I spend more time understanding:
- what sequence created the issue
- what state the system was in
- why recovery didn’t happen automatically
Only then do we change the flow.
The interesting part is that debugging these issues slowly changes how you design systems.
You stop building only for normal operation.
You start building for investigation too.
Because eventually, every long-running system develops behaviors you didn’t predict.
At BrainPack, a lot of debugging work involves understanding interactions between systems that only fail under very specific timing conditions. The more AI workflows and automations are layered on top, the more important observability and recoverability become.
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