AgentPing for product teams
An AI feature does not fail the way a service fails. It returns well-formed output, a 200, and a slightly worse answer than it did last month. There is no exception to catch, so nothing pages anyone, and the first real signal is a support inbox or a retention chart. AgentPing scores live runs so degradation shows up as a line on a chart with a deploy marker next to it.
You changed nothing. The output changed anyway.
→A provider-side update shifts behaviour on the ambiguous inputs first. Your eval set is built from clean examples, so it passes. The regression lands only on the messy real traffic that the suite never contained.
Ninety cases, written eight months ago, still passing.
→Since then a new segment arrived, onboarding copy changed and a feature routed a new category of input into the same agent. The suite tests the old distribution faithfully and reports health for traffic that no longer exists.
Being wrong more often does not generate tickets.
→Users do not report a feature that is subtly less useful. They use it less, then stop, and the decline gets attributed to seasonality because there was never a quality number next to the retention number.
Score a sample of live runs against a rubric, per feature and per customer, on the same record that carries cost and status.
Sampled scoring on real runs, not on a fixed suite. A trend line with deploy markers turns a five-week mystery into a two-day diagnosis.
A feature that silently stops producing output is the fastest quality regression there is. Freshness alerting catches the runs that never happened.
Quality and cost on the same run, so "use a bigger model" becomes a measured trade rather than an argument between two people guessing.
Score one feature on live traffic for a fortnight and compare it against what your eval suite says. The gap is usually the interesting part.