Observability.
Observability is the property of a system that lets you understand its internal state from its outputs — logs, metrics and traces — including for conditions nobody anticipated when it was built.
Where Observability fits on a long engagement.
The distinction from monitoring is about unanticipated questions. Monitoring answers questions you thought of in advance: is CPU high, is the error rate up. Observability is being able to ask a new question — why is this specific customer's request slow on Tuesdays — without deploying new instrumentation first.
That capability comes from high-cardinality structured data. Logs and traces carrying the identifiers that matter — user, tenant, request, version — allow arbitrary slicing after the fact. Aggregate metrics cannot answer those questions no matter how many dashboards are built from them.
What an assigned team does with Observability.
AI systems raise the stakes because they are non-deterministic. Without the prompt, the retrieved context and the response recorded for each call, a reported failure cannot be investigated at all — there is no reproduction to run.
Designing that in from the start rather than adding it after an unexplainable incident is part of what is scoped under ai engineering services.
What we use Observability for.
- New questions answered without redeploying High-cardinality data allowing arbitrary slicing after an incident starts.
- Non-deterministic systems made investigable Prompt, context and response recorded, because there is no reproduction otherwise.
- Correlation across services Identifiers carried through, so one request is followable end to end.
How Observability capacity is assigned.
Observability is assigned across platform and application capacity, designed in at the start because retrofitting it after an incident is when it is least available.
Tell us what your roadmap needs Observability for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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