Episode 1: AI Observability

AI observability is the continuous practice of measuring what AI systems do once they're live: the data they read, the outputs they produce and the actions they take. It's what turns AI from a black box you hope is working into a system you can measure, explain and steer. Your AI agent just sent a refund to the wrong customer — and your current monitoring didn't catch it until hours later. That's the gap between AI monitoring and AI observability. Monitoring tells you a metric crossed a line. Observability tells you why, traces it to the cause, and connects it to who owns the system and what it's allowed to do. In this video, Michael Rahm, Director of Product Marketing at Collibra, breaks down how AI observability works in four moves: 1️⃣ Register every model and agent at deployment — you can't observe what you haven't recorded 2️⃣ Align the signals — accuracy, latency, tool calls, data access and decision traces 3️⃣ Compare live behavior against a known, correct baseline so drift surfaces as an alert, not a customer complaint 4️⃣ Trace, notify and intervene when something breaks threshold — pause an agent, don't just log that it misbehaved Collibra's AI Command Center — part of the Enterprise AI Control Plane — brings this to life with code-first registration, an AI Trust Score, and cross-platform traceability across Azure, Snowflake, Databricks, Vertex AI and SageMaker, so teams can observe and govern every model and agent from one place. 📖 Read the full breakdown, including AI observability vs. AI monitoring, the metrics that matter, and what to look for in a tool: https://www.collibra.com/blog/ai-observability-explained-how-to-monitor-models-and-agents-in-production-from-one-command #AIObservability #AIGovernance #AIAgents #Collibra #DataGovernance #ResponsibleAI #MLOps #AICommandCenter