AI Agent Observability with Laminar Tracing

Building AI agents is only half the battle; understanding what happens inside their complex decision-making loops is crucial for production success. This written course guides you through the fundamentals of AI agent observability, teaching you how to integrate Laminar tracing to monitor message generation and evaluate performance. You will learn to: * Understand the core principles of LLM observability and telemetry * Configure Laminar tracing to track nested agent decisions and message flows * Initialize telemetry components and import essential packages in your codebase * Perform offline evaluations to measure agent accuracy and response quality * Analyze trace logs to debug latency and token usage bottlenecks Starting with foundational observability concepts, you will progress through step-by-step code explanations and structured implementations, learning to integrate telemetry and run offline evaluations on your agent workflows. This course is designed for software developers and AI enthusiasts who are new to agent monitoring, with no prior observability experience required. Begin reading today to bring transparency and reliability to your AI agent projects.