Evaluating AI agents in production tends to focus only on positive results. Did the agent complete the task? Was the output accurate? Did the demo go well?
The answers to those questions matter, but they miss the case that determines whether an enterprise can actually trust agents with real work: what happens in the 30% of instances where something goes wrong?
