A data‑science team unveiled a 12‑metric evaluation harness after a compliance officer questioned an AI agent’s hallucination risk. The framework, built over six weeks, monitors retrieval relevance, recall, precision, latency, answer faithfulness, hallucination rate, tool selection, execution success, multi‑step coherence, cost per query, and end‑to‑end latency.

Drawing from 100+ enterprise deployments, the authors set hard thresholds: retrieval relevance >0.85, recall >0.90, precision MRR >0.80, retrieval latency p95 <200 ms, hallucination <2 %, tool accuracy >0.92, and cost <$0.05 per query. The tool fills gaps left by unit tests and manual reviews, ensuring production readiness before launch.

Teams that ship without such a harness risk costly regressions. The article argues that adding evaluation retroactively costs 4–6 weeks and blindsides teams to real‑world failures. By embedding these metrics into the pipeline, engineers can detect drift, mis‑chunking, or embedding mismatches early, keeping user trust and compliance in check.