AI Agent Evaluation and Telemetry with Laminar

Building AI agents is only the first step; ensuring they behave reliably in production requires robust monitoring and evaluation. This course introduces you to the essential practices of tracing, measuring, and optimizing your agentic workflows. You will transition from guessing how your AI agent performs to systematically measuring its outputs, identifying bottlenecks, and tracking latency. What you'll learn: - Understand the foundational concepts of AI agent evaluation (evals) and observability. - Generate high-quality synthetic data to test your agents against realistic scenarios. - Configure OpenTelemetry with Laminar to trace agent execution steps and monitor performance. - Apply modern evaluation patterns to score agent outputs and identify failures. - Analyze telemetry traces to debug agent errors and optimize prompt performance. The course begins with core definitions and the fundamentals of evaluation metrics before moving into synthetic data generation and tracing configurations. You will read detailed explanations, analyze code snippets, and work through conceptual exercises designed to solidify your knowledge of AI observability. This course is designed for software developers, aspiring AI engineers, and tech enthusiasts who want to build reliable AI applications. No prior experience with Laminar or telemetry is required. Start reading today to master the science of AI agent observability.