Index listing of all 11 items tagged with #observability.
What to log in an AI system so its behaviour can be investigated later: how monitoring, audit and replay differ, and where a record stops being proof.
Agentic systems become trustworthy when they can pause, verify, and escalate instead of only continuing.
Silent degradation happens in small increments that escape macro monitoring.
In LLMOps, evaluations are continuous operational contracts rather than static benchmark milestones.
Measurement must establish a baseline before optimization begins, to avoid scaling noise.
Without traces and verification signals, teams repeat the same failure with new words.
Quality checks must run continuously at runtime to adapt to shifting user inputs.
The debugging session that taught me why observability is not optional in orchestration, and what I now look for first when a multi-agent system misbehaves.
A small review ritual for checking whether my AI workflows are getting clearer or only getting faster.
A small weekly ritual that keeps my AI workflows honest after launch.
A compact weekly review format for tracing decisions, evidence, and outcomes in AI workflows.