AI observability is the practice of monitoring models and agentic systems in production — tracking quality, latency, cost, drift, and safety signals.
Traditional software metrics are not enough when outputs are probabilistic and prompts, tools, and data all change over time.
What to observe
- Input/output quality and evaluation scores
- Latency, token usage, and spend
- Drift in data and behavior
- Tool failures and policy violations
- User feedback loops
Observability turns AI from a black box into an operable product — essential for trustworthy automation at scale.
