As AI workflows span multiple models, agents, tools, and decision points, understanding what happened, why it happened, and where things went wrong becomes increasingly difficult. Traditional monitoring approaches often provide only a partial view of system behaviour, leaving teams with significant operational blind spots. This panel examines how organizations are building traceability and observability into complex AI systems. We'll discuss approaches to tracking workflow execution, investigating failures, monitoring interactions across components, and improving visibility into increasingly autonomous applications. Attendees will gain practical insights into the tools, techniques, and operational practices helping teams better understand and manage AI systems in production.