AI Observability Explained: Traces, Spans, and What to Watch
Published
Learn the essentials of AI observability and why it's critical for monitoring agent performance, debugging errors, and improving your AI product. Discover how tracing and evaluations work together to create a self-healing loop for your development workflow. ā± Chapters: 0:00 Introduction to AI Observability 0:44 Core Concepts: Traces and Spans 3:11 Observing Agentic Workflows 5:49 Evaluations and Observability 8:16 Implementation and Best Practices 10:10 Avoiding Observability Pitfalls 11:57 Extracting Insights and Self-Healing 15:12 The Observability Landscape š Keywords: #AIObservability #LLMOps #Tracing #AI #MachineLearning #AgenticAI #Monitoring --- Brainforge helps teams design, build, and run AI data systems. Ask your data anything with Snowflake Cortex.