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GuideFebruary 10, 202611 min read
Observability for AI Applications: What to Track and Why
Most AI apps skip the operational layer. Here is the minimum instrumentation that makes production workable: latency, confidence, failure paths, and cost.
ObservabilityProductionGuide
This post is part of the Learning Log archive. The architecture and key decisions from this piece are summarized in the header above.
The core theme is practical: production AI systems are defined by operational discipline — observability, fallback behavior, cost controls, and the interfaces between components — not isolated model quality.
The full write-up is being migrated into the new publishing flow. Check back soon.