AI Observability
The ability to monitor, analyze, and understand AI system behavior using logs, metrics, traces, and performance insights throughout the model lifecycle.
What is AI Observability?
AI observability provides visibility into what happens throughout an AI system during real-world use. It can involve monitoring prompts, responses, model performance, latency, errors, user interactions, tool calls, and security events. By combining logs, metrics, traces, and other signals, teams can investigate unexpected behavior and understand how AI applications perform over time.
Why is AI Observability Important?
AI systems can behave unpredictably as models, users, and data change. Observability helps organizations detect performance degradation, security threats, policy violations, and unusual behavior earlier. It also supports troubleshooting, auditing, governance, and continuous improvement of AI applications.
Common use cases
AI observability is commonly used for monitoring LLM applications, AI agents, production models, prompt activity, model performance, security events, and compliance requirements.