Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach

Complex LLM-powered agents require observability beyond traditional logging to trace decisions, evaluate response quality, and detect drift. A minimal-code, configuration-first approach can achieve this by integrating an observability platform. The content demonstrates building a multi-agent customer service application using LangGraph and integrating it with the open-source Langfuse platform. This setup enables capabilities like LLM-as-a-Judge for automated response scoring, regression testing to detect drift, and detailed tracing of metrics, events, and logs without extensive instrumentation code.