The advancement of AI products has moved beyond single-turn LLM calls to more intricate systems powered by autonomous agents and complex applications, necessitating enhanced monitoring and debugging capabilities. Traditional logging methods fall short in addressing issues like context drift and inefficient reasoning within these dynamic, stateful systems, which handle multiple turns and decisions. LLM observability fills this gap by providing detailed, real-time visibility into every layer of an LLM-based system, from input to output, enabling teams to analyze latency, cost, correctness, and quality. This involves using traces and spans to track the journey of requests and sessions to evaluate interactions over multiple turns. Tools like OpenInference and OpenTelemetry facilitate this process by capturing detailed telemetry, while platforms like Arize AX and Arize-Phoenix offer comprehensive solutions for monitoring and optimizing AI agents. These tools enable the proactive identification and resolution of performance bottlenecks, thereby ensuring reliable and efficient AI applications.