How Testing Works

How teams test AI agents, and where it breaks.

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.

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The current methods

How AI agents get tested today

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.

Ground truth comparison
Write the expected correct output for each input, run the agent, and compare the output against what you expected.
Good forDeterministic scenarios with one right answer, like "this question should generate this SQL query."
Breaks whenThe customer asks something ambiguous or conversational that has no single correct output to compare against.
LLM as judge
Send the agent's output to another model and ask it whether the response was good.
Good forA fast, cheap first read when you have no rubric and just want a rough signal.
Breaks whenYou need consistency. With no structured rubric, the verdict shifts run to run and changes with phrasing.
Regression tracking
Run the same tests after every build and compare the scores to the previous version. If scores drop, the build failed.
Good forCatching a change that broke something you already had a test for. A genuinely good discipline.
Breaks whenThe failure is outside the test set. Every scenario was written by engineers testing for what they expect, not what they don't.
A dedicated QA team
Staff a team to build and maintain a full testing framework in house, writing and updating the scenarios by hand.
Good forDeep, bespoke coverage of the paths your team knows matter most.
Breaks whenYou count the cost. That is headcount spent maintaining QA infrastructure instead of building the product.
The shared blind spot

Three gaps every method above shares

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.

01

You can only test what you imagined

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.

02

Most questions have no single right answer

Ground truth works for a lookup. Conversational, ambiguous, multi-part requests need judgment, and judgment cannot be diffed against an expected string.

03

They test one turn, not the conversation

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.

What we do instead

Test the conversations nobody scripted

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.

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