LLM Observability for Building Reliable AI Applications

LLM applications fail differently from traditional systems: plausible but incorrect outputs, opaque multi-step reasoning, and unpredictable token costs make debugging difficult with standard tools. As AI applications become core to customer-facing workflows, we need observability tools that go beyond traditional measures of system health to evaluate model behavior and analyze token usage as well. This webinar introduces LLM Observability and is intended for engineers building and operating LLM-based systems in production. We’ll demonstrate three common use cases for LLM Observability in Observe: • Quality of AI responses - Inspect individual LLM calls within agent traces to understand how prompts, tool selections, and intermediate outputs contributed to incorrect or low-quality responses. • Token usage - Analyze token consumption patterns to detect cost regressions and inefficiencies. • Troubleshooting AI infrastructure - Trace application failures through the full stack, connecting AI app data to infrastructure and APM data.