AgentPing for product teams

Ship AI features
that stay good.

A feature that worked at launch can quietly get worse with the next prompt change or model update. AgentPing scores production output, ties the trend to the changes you shipped, and shows the cost and reliability behind each feature, so you ship on evidence and catch regressions before customers do.

Track your first AI workflow
summariser · judge score↓ 4.2 to 3.8
  • cites a source pass
  • answers the question pass
  • stays on policy fail

What product teams need to ship with confidence.

Quality is the headline, but it only means something next to cost and reliability. The same run record carries all three for every feature you ship.

Output quality and drift

A quality score per feature, trended over time, so a regression after a change shows up the day it ships.

summariser · judge score↓ 4.2 to 3.8
  • cites a source pass
  • answers the question pass
  • stays on policy fail

Explore Verify →

Cost per feature

See which AI feature has poor margins before you ship it wider, attributed to the feature you tagged.

spend · this month↑ on budget
$5,382
spent
$0.094
cost / successful run
content-writer$2,189
research-agent$1,474
support-triage$685
email-classifier$262

Explore Spend →

Reliability of the feature

Know when a feature stalls or stops running, not when a customer tells you it is broken.

support-triage · schedulelive
support-triage missed its 14:00 run paged on-call · last ok 13:00 · 247 runs clean before

Explore Pulse →

How do product teams measure AI feature quality?
AgentPing scores production output two ways: deterministic checks on every run at no model cost, and a plain-English rubric scored by an LLM-as-judge on a sample. You get a quality number per feature, trended over time, instead of a gut feeling.
How do we know a prompt change made things worse?
Prompt changes are recorded alongside the quality trend, so a drop has a cause. When a tweak ships and the score slides, you see it on the day rather than in the support queue a week later.
A run returned 200. Why track quality at all?
Because a successful response can still be wrong: a summary that drops a section, JSON that loses a field, an answer that drifts off policy. Status codes do not watch the thing your customer actually reads. Quality scoring does.
Does this connect to what the feature costs?
Yes. The same run record carries cost and reliability, so you can weigh a feature's quality against what it spends and whether it runs cleanly, and decide what to ship wider on evidence.

Catch AI regressions before your users do.

Score one feature's output, write a rubric in plain English, and watch the trend instead of waiting for the support queue.

Track your first AI workflow

For SaaS teams → For platform teams → All features →