What is observability?
Understanding a system's internal state through logs, metrics and call traces, so the cause of a problem can be found quickly.
In detail
The more complex a system, the harder it is to pinpoint which link failed when something goes wrong. Observability requires systems to actively record key information: how long each request took, where errors occurred and which services were called. For AI applications, this also includes logging model inputs, outputs and the tools called.
For example
A customer reports slow order syncing; using the call logs, the operations team finds that one interface's response time has risen from 0.2 seconds to 5 seconds.
How companies use it
Before go-live, plan what to record, how long to keep it and who to notify about anomalies. Don't wait until something goes wrong to fill the gaps.
Common misconceptions
Collecting masses of logs that nobody reads. Pair them with alert rules so that important anomalies reach the right people proactively.
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