AI Observability
The practice of collecting, analyzing, and visualizing technical telemetry from AI systems to understand their operational behavior.
Full Definition
AI Observability refers to the infrastructure and practices for collecting, analyzing, and visualizing technical telemetry from AI systems in production. This includes monitoring metrics like latency, throughput, error rates, token usage, and model performance over time. Observability provides the raw operational data that enables teams to debug issues, optimize performance, and understand system behavior. While critical for operational excellence, observability alone is insufficient for governance — it reveals what the system is doing technically but not whether those actions comply with policies, regulations, or ethical standards. Effective AI management requires observability for operational health combined with governance for compliance and accountability.
Related Terms
AI Governance
The framework of policies, processes, and technologies used to ensure AI systems operate ethically, transparently, and in compliance with regulations.
Model Monitoring
Continuous tracking of AI model performance, behavior, and output quality in production environments.
Anomaly Detection
The automated identification of unusual patterns or behaviors in AI agent operations that deviate from expected norms.