Here is the honest situation. Here is the honest situation. Most teams can build an agent faster than they can tell whether it is getting better, and the gap between those two speeds is now the bottleneck on the whole product. Typing eight requests into a new prompt and liking the answers is not testing, it is sampling your own optimism, because an agent produces a distribution of behaviour and the failures cluster where the builders do not look. Doing this properly means naming the small set of journeys that must never regress, assembling an evaluation set out of real traffic including the failed and abandoned runs, comparing a candidate against the incumbent over the same inputs, reading the tail rather than the mean, sizing a sample against the effect you would actually act on, calibrating an automated judge against human labels instead of trusting it, spending scarce human review where it changes a decision, and instrumenting the failure modes that belong to agents specifically: tool misuse, looping, silent truncation, confidently wrong answers, drift after a model change. Where teams fall short is predictable: a happy-path evaluation set that has scored in the nineties for a year, a threshold written the day after the result arrived, and a one-line prompt edit that shipped because it was only one line.
This Kit removes the guesswork. It is AI agent quality assurance written as adopt-ready controls you personalize in a weekend, with the evidence a release reviewer examines.
What you get, the moment you buy
Grounded in how agent products are actually evaluated and where those evaluations actually fail. Editable Word and Excel files.
What one control looks like
This is the opening control, where the method begins. All 18 are built to this depth.
Why this is not another template pack
- The evidence is the point. A control you cannot evidence is a gap waiting to be found in a release review. This tells you what a reviewer examines and where teams fall short, for every control.
- The agent specifics built in. Baseline journeys with checkable success conditions, held-back evaluation sets, paired cohort comparison, sample sizing and stopping rules, judge calibration against human labels, and the named agent failure modes are written into the controls, not left generic.
- Built on real practice, not one person's opinion, grounded in how agent evaluations actually hold up under a release decision and where they actually fall apart.
- It compounds. This work shares its shape with experimentation, model risk and product quality practice, so it feeds the way your team ships everything else.
Who buys this
Product managers, QA engineers, ML engineers and engineering leads who have to say whether a new agent version is better than the last one, and the data and research partners who supply the numbers behind that call. Whether this is your first evaluation framework or a maturity uplift, you save weeks and walk in with your journeys, evaluation set, comparison, judging, review and release-gate controls structured.
Common questions
Is it really editable? Yes. Word and Excel files you own and adapt. No portal, no subscription.
Does it cover automated judging? Yes. Calibrating the judge against human labels, writing rubrics as behavioural checks, and handling the self-agreement risk when judge and agent share a model family each have their own control with its own evidence.
Does it cover the statistics? Yes, stated correctly and qualitatively. Sizing a sample against the effect you would act on, fixing the stopping rule in advance, decomposing without fishing, and separating a detectable difference from a worthwhile one are all built as controls.
What if it is not for me? A 30-day money-back guarantee.
Instant digital download · 30-day money-back guarantee · The Art of Service Pty Ltd, GPO Box 2673, Brisbane QLD 4001 · support@theartofservice.com