Why LLMs Need Observability and How to Do It

Large language models are rapidly becoming core building blocks of modern applications, powering chatbots, developer tools, search, analytics, and autonomous agents. While getting a prototype running is relatively easy, operating LLM-powered systems reliably in production presents a very different challenge. These models are probabilistic, non-deterministic, and highly sensitive to inputs, context, and upstream dependencies. As a result, traditional monitoring approaches often fall short, leaving teams with limited visibility into why systems fail, degrade, or behave unexpectedly.

This session explores why large language models need observability and, more importantly, how to implement it in practice. We will start by examining the unique operational challenges of LLM-based systems, including hallucinations, prompt drift, silent quality regressions, unpredictable latency, escalating token costs, and complex multi-step inference pipelines. Unlike traditional services, failures are often subtle. The system may appear to be running, yet the output can be incorrect, misleading, or untrustworthy.

This session dives deep into LLM observability, the methods and tools needed to truly understand, monitor, and improve large language model systems in production. We will explore practical architectures and patterns for instrumenting LLM pipelines, from single-model deployments to complex agent-based and retrieval-augmented workflows.