Eval Dashboards
A live dashboard showing how well every AI workflow is doing — quality score, failures, speed, cost — so nobody has to wonder whether things are getting better or worse.
Think of a report card that updates itself every day — grades per subject, trend lines, and a note when something slips. Eval dashboards are that for your AI workflows: the test scores, failures, speed, and cost on one page everyone can see, instead of buried inside the test machinery.
The problem, in plain words
Someone asks a simple question in a meeting: is the AI getting better or worse? Silence. The tests exist, somewhere. Someone ran them recently, probably. Without a visible scoreboard, quality is a matter of impressions — and there's a quieter danger underneath: evals nobody looks at gradually stop being run at all. The discipline erodes exactly because no one can see it eroding.
What we set up
Dashboards at two levels — one per workflow, one for the whole portfolio — showing the eval-set score over time, the regression count (old bugs re-tested on every change), latency at the p95 mark (the time within which the slowest 5 percent of requests still finish), cost per request, and the most recent clusters of failures. Every failure on the board links through to its trace (the recorded play-by-play of what the agent did), so 'the score dropped' is one click away from 'here is exactly what went wrong'. Each workflow has a named owner who receives a weekly summary — and a real decline doesn't wait for the weekly note: it pages someone (sends an urgent alert) right away.
How it works, step by step
- Every eval run reports in
Each test run's score, regressions, latency, and cost land on the dashboard automatically — no one compiles a status deck.
- Trends, not snapshots
The lines over time are the point: is this workflow improving, holding steady, or quietly slipping?
- Failures are one click deep
From a bad number, to the cluster of similar failures behind it, to the exact trace of a single one.
- Owners get a weekly summary
Each workflow has a named person responsible, and that person gets the digest — quality with an address.
- Real declines page someone
A genuine drop doesn't wait until Friday. It triggers an alert the moment it's detected.
What changes for you
Before, quality was the private state of the test machinery, and whether the evals even ran depended on habit. After, quality has a visible owner and a visible trend, the meeting question 'are we getting better?' has a one-glance answer, and the eval discipline itself stays alive because everyone can see it working. What it won't do: it won't improve anything by itself. A dashboard is a smoke detector, not a sprinkler — someone still has to act on what it shows.