AI deployment, our client’s compliance officer asked us a question we couldn’t answer. “How do you know your agent isn’t hallucinating patient symptoms?” We had unit tests. We had integration tests. We had a model that performed beautifully on the demo dataset. What we didn’t have was an evaluation harness that could measure hallucination rate, context faithfulness, or tool-selection accuracy in production. That gap nearly killed the project. Six weeks later, we had a 12-metric evaluation framework running against every agent response, every tool call, every retrieval operation. The compliance team signed off. The agent shipped. Across the 100+ enterprise AI agent deployments we’ve shipped since then, that framework has evolved into the playbook below. If you’re building production AI agents, this is the evaluation harness we wish we’d had on day one. The 12-Metric Framework at a Glance Category Metric What It Measures Critical Threshold Retrieval Context Relevance Are retrieved chunks relevant to the query?…