AI observability vs monitoring: What's the difference?

AI observability and monitoring, though often used interchangeably, serve distinct purposes, especially in the context of AI systems. Traditional monitoring involves tracking predefined metrics and alerting on threshold breaches, effectively catching predictable failures in systems like CPU usage or error rates. However, it falls short in identifying failures unique to AI, where systems can appear operational yet deliver incorrect or unsafe outputs. Observability, on the other hand, provides a deeper understanding of a system's state through logs, metrics, and traces, allowing for investigation of unexpected behaviors without prior anticipation. AI observability builds upon this by adding evaluations to assess the quality and safety of AI outputs, addressing the probabilistic nature of AI responses that can vary significantly with the same input. This approach is crucial for ensuring AI systems deliver accurate and compliant outputs, as traditional monitoring fails to catch silent AI-specific errors. AI observability is particularly vital in agentic AI systems, where the implications of incorrect actions are significant. Twilio's Conversation Intelligence exemplifies AI observability by analyzing live interactions in real-time, detecting errors, and auto-escalating to human agents when necessary, thereby bridging the gap left by conventional monitoring methods.