Building evaluation automation end-to-end

By this point you have three pieces that work on their own: the offline pipeline from 5.1 that scores and stores every run, the gate check from 5.4 that runs before each rollout step, and the monitoring logic from 5.5 that watches production for sudden drops and slow drift. This lesson connects them into one loop, and new failure patterns from production go back into the test set on the rotation schedule from 5.2.

The weak spot is the monitoring piece, because it runs once a day. A regression that starts at 2 PM on a Friday shows up in Saturday’s 6 AM report, and someone reads it at 9. That’s 19 hours of wrong answers before anyone on the team knows. The logic is fine. It runs on the wrong schedule.

The fix has four steps. Send every trace to one place the team can query, capturing all of them during a ramp and 1 to 5 percent once things are stable. Build the dashboard from your own metric specs, SQL success rate and P95 latency split by segment, and ignore the platform’s defaults. Split alerts into blocking alerts that page someone and awareness alerts that wait for business hours, and set the alert line from normal day-to-day variation so the team doesn’t learn to ignore it. Then run a drift detector all the time, so a slow slide gets flagged before it crosses the blocking threshold.

All of it feeds one decision: ramp, hold or roll back.

For the practice you set up monitoring for one AI feature. If you have a free account on a monitoring platform and some logged traces of your own, with a timestamp, a success flag and a latency, load 100 traces, build the two charts, set one blocking and one awareness alert, send a test alert, and add a drift detector. If you don’t, write the same setup as a design spec.