Traditional monitoring strategies struggle to effectively manage AI applications due to the unique challenges posed by AI systems, such as non-binary success states, context-dependent performance, and complex error attribution. AI applications require specialized monitoring frameworks that evaluate multiple layers, including input characteristics, model behavior, output quality, and user experience, to provide comprehensive insights into system health. Effective AI observability involves detailed request-level tracing and semantic monitoring to ensure outputs are meaningful and appropriate. Performance analytics must assess response time distributions, success rates, and cost analysis, while real-time alerting should focus on quality degradation, cost spikes, provider dependencies, and safety violations. The Braintrust platform exemplifies an approach tailored for AI observability, offering infrastructure for comprehensive monitoring and evaluation, integrated evaluation workflows, and performance dashboards that address the specific needs of AI systems. Building observability into AI development workflows from the start is critical to ensuring AI applications deliver value and meet business objectives.