Glossary · AI-native development
Observability
The ability to understand an AI system's behavior from recorded inputs, outputs, state transitions, tool calls, timings, costs, errors, and evaluation signals.
Why it matters
AI failures often span model, retrieval, tools, and orchestration. You need correlated evidence to locate the failing boundary.
In practice
Record a trace ID across retrieval, model calls, tool execution, approvals, and final scoring while applying redaction and access controls.
Common confusion
Logging collects events. Observability makes those events structured and connected enough to answer operational questions.
Related terms
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