AI & automationMarch 20267 min read
Measuring an AI agent in production
An agent that worked in the demo can drift silently in production. Without continuous measurement, you will only find out the moment a client sees it.
On launch day, everything works. Three weeks later, the data has changed, the usage has too, and the agent answers less well without anything signaling it. That is the classic trap of AI in production: no crash, just quality quietly eroding.
The metrics that reassure you for the wrong reasons
Response time and the technical error rate are useful, but they say nothing about the correctness of the answers. An agent can be fast, never crash, and still be wrong half the time.
The metrics that matter
We track the real quality of the answers on a continuously evaluated sample, the escalation rate to a human, and the cases where the agent makes things up. Those signals warn you before the client ever notices.
An agent you don't measure is not in production. It is a gamble.