Building an Evaluation Harness for Production AI Agents: A 12-Metric Framework From 100+ Deployments

Many AI agent projects fail in production not due to model issues, but because of inadequate evaluation systems. A robust 12-metric framework, drawn from over 100 enterprise deployments, offers a playbook for catching these failures before they ship to users. The framework evaluates performance across four key areas: the quality of retrieved information, the faithfulness of generated answers, the agent's tool-use accuracy, and critical production metrics like cost and latency. By continuously tracking metrics such as context relevance, hallucination rate, and tool selection, teams can ensure their agents are reliable, trustworthy, and efficient.