There are a handful of real ways to test an AI agent. Each is good at something. They share one blind spot: they can only test what someone thought to write down.
These are the real approaches teams use. None of them is wrong. Each one is built for a specific kind of check, and each one has a place it stops working.
However you build it, testing that runs pre-written scenarios has the same three limits. This is the layer that loses deals, because it is the layer nobody is watching.
Every method covers the scenarios someone wrote down. None of them covers the conversation no one thought to anticipate, which is where real customers live.
Ground truth works for a lookup. Conversational, ambiguous, multi-part requests need judgment, and judgment cannot be diffed against an expected string.
A ground truth test checks one input and one output. It never tests turn 4, when the customer contradicts what they said at turn 2.
We generate adversarial synthetic customers modeled on real buyer behavior and run them at your live agent. They push on price, change their mind, contradict themselves, and go quiet, the way real customers do. That produces the conversations no test suite was written for.
Every conversation is scored against a published 10-dimension rubric per industry, so the grade means the same thing every run and you can check exactly how it was reached. Each test also returns a business-outcome pass or fail: did the agent actually do its job.
Closes gap 1. Adversarial personas create the conversations you never scripted, so you test what you did not think to write down.
Closes gap 2. A structured, published rubric scores judgment consistently, which is the calibration an ad hoc LLM judge never has.
Closes gap 3. Real multi-turn conversations score context retention, repetition, and objection handling under pressure, turns deep.
The other methods still have a place. Ground truth is right for deterministic checks. Regression discipline is worth keeping. We cover the conversational, adversarial, multi-turn layer they cannot reach, and we do it as a benchmark you run, not a team you staff.
Conversational testing is only half of it. When an agent answers questions from your data, your CRM, your tickets, your logs, a fluent answer can still be wrong. To catch that, you have to know the correct answer. That is ground truth, and for a data agent it is not optional.
Ground truth is exactly the right method here. The problem is producing it. Most teams compute the expected answer for every test question by hand, or staff engineers to maintain a growing test dataset. It does not scale, and it never covers every case.
We generate a synthetic version of your system from your schema. Because we generate the data, we compute the correct answer to every question automatically. Ground truth is generated, not hand-written. We have not found anyone else doing this for agent testing.
Automatic ground truth. We generate the correct answer for every question from your schema, so no one hand-computes expected results or maintains a test dataset by hand.
Your data stays yours. You share your structure, not your records. The synthetic system matches your schema without touching real data.
Adversarial by design. Questions span 7 categories, from ambiguous and multi-step to false-premise, so you test the queries a builder would never think to write.
Benchmark your AI agent against the same rubric. Free, no account required.