What is AI observability (and how does it work)?

AI observability is an advanced practice designed to monitor AI models and agents in production, ensuring they produce correct, safe, and useful outputs, which traditional monitoring systems cannot fully capture. Unlike standard metrics that focus on system uptime and latency, AI observability evaluates the quality of AI outputs, checking for issues like hallucinations, script violations, and compliance with brand standards in real time. It consists of four core components: traces, metrics, logs, and evaluations, with evaluations being unique to AI systems. In the customer service context, AI observability is crucial as it monitors AI agents' behavior, allowing businesses to intervene in real time during conversations, thereby preventing potential compliance violations or customer dissatisfaction. Twilio's Conversation Intelligence exemplifies this by using generative AI to analyze interactions, detect risk signals, and enable immediate escalation to human agents, ensuring AI agents align with business operations and enhance customer experience.