Observability in AI systems means being able to answer: what did the model receive as input? What did it return? How long did it take? How much did it cost? Did it use the tools correctly? Was the output high quality?
Without observability, you're flying blind. You can't debug user complaints, can't measure whether a prompt change improved quality, can't detect when a downstream API change broke your tool-use logic.
The standard approach: log every model call with a unique trace ID, the full prompt (or a sanitized version), the response, latency, token counts, and any tool calls. Attach user/session metadata. Route logs to a dedicated observability platform — LangSmith, Arize Phoenix, Helicone, Braintrust, or a self-hosted setup on top of your existing log infrastructure.
One non-obvious practice: log the model version and temperature settings alongside every call. When a model provider silently updates a model, your logs become the only way to correlate the change with the quality shift.
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