Solution · AI Operations
Evaluating the quality of your AI agents: the function that measures, watches and corrects them in production — before the customer feels the error
An agent that passed the demo isn't an agent that works in production: it degrades on its own when the model, the prompt or the world changes, and nobody notices until a customer complains. Evaluating its quality isn't a test you run once; it's a function that runs — sample, score against a rubric, catch regressions and correct — so quality stops being an impression and becomes a number you watch.
The problem
An agent's quality isn't measured when it nails the demo. It's measured when it hesitates in production — and today nobody's watching.
- The agent passed the demo and got waved through; since then nobody knows if it still answers well, because there's no way to measure it short of waiting for someone to complain.
- You changed the model, tweaked a prompt or the vendor updated on its own, and the behavior shifted with nobody comparing it to before: the regression gets discovered in a customer, not in a test.
- The only quality signal is complaints, which arrive late and only from the tip of the iceberg; the silent errors — the plausible but wrong answer — nobody sees.
- When it's time to decide whether a change ships, there's no number to back it: it goes out on gut feel and a prayer, because there's no criterion to score against before letting go.
Cost of staying the same
An agent with no evals isn't a system, it's a bet that degrades on its own. The cost isn't just the one-off error: it's not knowing whether your agent is better or worse than last week, and therefore being unable to improve it or defend that it works. When the only metric is the complaint, every regression is paid by a customer before a dashboard, trust in the agent falls and the default reaction is the worst — switch it off or leave it languishing under manual watch, which is throwing away the investment. And since the model, the prompt and the world change every few weeks, quality validated three months ago says nothing about today's.
The solution
We set up and run your agents' evaluation function — continuous evals: sample, score, catch regressions, gate and correct — so quality stops being an impression and becomes a number you watch and defend
- 1We define the criterion: what a good answer is for your case — accuracy, tone, safety, format, policies — turned into a rubric and a set of test cases with the correct answers, yours, not generic.
- 2We sample and score continuously: we take real production conversations, evaluate them against the rubric — with automated judges and human review where it matters — and turn quality into a metric tracked over time, not an anecdote.
- 3We catch regressions and drift: every model, prompt or tool change is scored against the criterion BEFORE it ships (gate), and in production alerts fire when quality drops, even if nobody has complained yet.
- 4We close the loop: every failure caught feeds corrective actions — prompt tweak, new example, guardrail — and goes back into the test set, so the agent improves instead of degrading. All measured: rubric pass rate, regressions caught before production, drift detected and time to fix.
What changes
What you stop losing
Quality stops being an impression: it becomes a metric tracked over time, so you know whether the agent is better or worse than last week before a customer tells you.
Mechanism
Regressions get caught before production: a model or prompt change is scored against the criterion and only ships if it doesn't get worse, instead of discovering the fault in a customer.
Mechanism
The silent error stops hiding: continuous sampling surfaces the plausible-but-wrong answer that complaints don't capture, because almost nobody complains — they just leave.
Mechanism
What we measure: rubric pass rate, regressions caught before production, drift detected continuously and time from failure to fix.
What we measure
Spec sheet
- Work it removes
- that the quality of your AI agents in production is measured by nobody and only surfaces when a customer complains, with no criterion, no sampling and no gate before shipping a change
- Typical setup
- 2–4 weeks
- Input
- the real conversations of your agents in production, the model, prompt and tool changes that ship, and the complaints that arrive late and only from the tip of the iceberg
- Output
- each agent's quality scored continuously against your own rubric, with regressions caught before production, drift detected with an alert and a correction loop that feeds back into the test set
- Works with
- OpenAIAnthropicAzure OpenAIAmazon BedrockGoogle Vertex AI
- Can connect to
- Your agents and their production recordsYour eval frameworks (LangSmith, Promptfoo, Braintrust)Your quality dashboard and your deployment gate
- What we measure
- rubric pass rateregressions caught before productiondrift detected continuouslytime from failure to fix
- Good fit for
- companies with one or more AI agents in production whose quality nobody measures systematically today and who need to govern it — measure it, watch it and defend it — before scaling
- Not a fit for
- those still in prototype with no real traffic to sample, or those wanting a single one-off audit instead of a quality function that runs continuously
Frequently asked questions
Testing it once is the launch-day snapshot; this is the film. An agent in production degrades on its own when the model changes, a prompt gets tweaked or the world moves, so a launch test says nothing about quality three weeks from now. What we set up runs continuously: it samples real conversations, scores them against your criterion and warns when quality drops. The initial test is a point; the function is the line.
You do, and we put it in writing before measuring anything. The rubric — accuracy, tone, safety, format, your policies — and the test set with the correct answers come from your business, not a template. We bring the method — automated judges for volume, human review where it matters — and the discipline so the criterion isn't made up by each person. Without a criterion of yours, 'quality' is an opinion; with it, it's a number.
Neither blindly nor not at all: we use them for what they do well — reviewing high volume against clear criteria — and calibrate them against human review on a sample, to know how often they're right. The delicate or ambiguous always goes to a person. The goal isn't to replace human judgement with a judge AI, it's to scale it: a human-reviewed sample sets the bar and the automated judge applies it to the rest, supervised.
With the ones you already have. We work on top of your agents and their production logs and on standard eval frameworks — LangSmith, Promptfoo, Braintrust and similar — whatever your model provider is. We don't ask you to rebuild the agent or switch tools: we set up the evaluation function on top of what already runs in production.
Want it running in your business?
You’ve pinned the problem. We ship the fix and leave it measured.