When deploying an agent via the Mosaic AI Agent Framework (which leverages Databricks Model Serving), operational metrics are captured automatically by default. These include system-level telemetry such as the number of requests per second (volume), the time taken for the model to respond (latency), and the rate of 4xx/5xx HTTP errors. These are essential for monitoring Service Level Agreements (SLAs). However, Quality metrics (B), such as correctness, groundedness, or adherence to custom guidelines, cannot be determined " automatically " by the serving infrastructure because they require either human feedback or an LLM-as-a-judge evaluation (using Databricks Agent Evaluation). While Databricks makes it easy to generate quality metrics using the mlflow.evaluate API or the inference table, they are not " default operational metrics " that appear without additional evaluation configuration.