Most agentic AI evaluation today lives in the terminal, precision scores, latency benchmarks, hallucination rates. It's rigorous and it's necessary, but it completely misses the question that actually matters to the business: Does this agent think the way our people think?
At Hazelheartwood, we build AI with small lean teams, and we test it the same way, treating every evaluation cycle as a chance to cut waste and validate early rather than over engineer. This masterclass introduces our approach to testing for genuine business impact, how we define and iterate toward minimum viable accuracy (MVA), and how our framework separates data accuracy from business accuracy, checking whether the agent reasons the way the business user it supports would. You'll also see where human in the loop validation fits, because in the end, AI that serves people has to be tested by them too. Attendees will leave with a framework they can apply to test whether their own agents reason the way their business actually does.