Evaluation is how you know whether an AI system is working. It covers pre-deployment testing to assess whether the model performs well on representative tasks, ongoing production monitoring to check whether quality holds over time, and regression testing when changes are made.
A claims triage agent tested against 500 historical claims with known correct classifications before deployment — showing 94% accuracy on straightforward cases and 78% on complex ones — gives the team concrete information about where to improve before going live. But evaluation does not stop at deployment. Models drift, data changes, and business requirements evolve. Continuous evaluation is what keeps production AI systems performing as intended.
