Observability
Observability is the ability to understand the internal state, performance, and behavior of a system by collecting and analyzing information about its outputs, events, and operational activity.
What is Observability?
Observability uses data such as logs, metrics, traces, events, and system outputs to provide visibility into how an application or AI system is operating. In AI systems, observability can include monitoring prompts, responses, model performance, tool calls, latency, errors, and other interactions across the AI workflow.
Why is Observability Important?
Observability helps teams detect problems, investigate unexpected behavior, and understand system performance. For AI applications, it can also provide visibility into model interactions and help identify issues such as unreliable outputs, security threats, performance degradation, or policy violations.
Common use cases
Observability is commonly used for AI applications, LLM monitoring, model performance tracking, debugging, security monitoring, incident investigation, and production system management.