Most evals ask one question. We ask dozens.
One question can only catch one kind of mistake. Real AI systems fail in more ways than that. That's why most evals miss the problems that matter, and flag problems that aren't really there.
Two mistakes. One root cause.
Any eval ends up in one of four places. Two are right, two are wrong. A quick, single-question eval tends to land on both kinds of wrong at once.
“A quick eval makes both mistakes at once: it misses real problems and flags fake ones. A high-dimensional eval shrinks both, so what you see is what actually matters.”
Three common approaches. None close the gap.
Each one helps with part of the problem. None of them catches the everyday behavior that causes the most damage: an agent that gives advice it shouldn't, drifts off-brand, or makes a subtly wrong call that no single check was built to catch.
LLM-as-a-Judge
Ask one AI whether another AI's answer looks risky. Fast and cheap, but one model asked one question gives you an answer, not the answer.
Runtime Safeguards
Basic filters that run as content is generated. Built strictly for milliseconds of speed, not for catching nuanced context or domain regulations.
Red Teaming
Trying to trick the AI into breaking its own rules. Good at catching jailbreaks, but doesn't touch the everyday operational mistakes that cause real business damage.
The gap is bigger than it looks.
It's hard to measure exactly how much a quick eval misses, since it depends on the model. But the direction is clear, and the two kinds of error don't cancel out.
Missing real problems and raising false alarms don't offset each other. They stack up, leaving a dangerous fraction of what your AI does completely untested.
What we do differently.
If asking one question is the problem, this is the fix. Four principles working in concert.
Break every risk into smaller checks
"Is this agent safe" isn't one question, it's four or five. When something fails, you find out exactly which check it failed and why, instead of receiving a single unhelpful aggregate score.
Show the reasoning, not just pass or fail
Every finding comes with the transparent reasoning behind it, providing the exact context required to fix the underlying issue instead of guessing why an alarm sounded.
Check with more than one model
No single model is an unbiased judge on its own. One flags too aggressively, another ignores edge cases. An ensemble of specialized judges eliminates blind spots.
Build every standard with real risk experts
Not synthesized rules guessed by a general-purpose model. Standards crafted in partnership with top legal and compliance practitioners who understand real-world liability.
One risk. Five smaller checks.
The rule: a support agent should never exceed its authorized scope.
Overstepped its role: Offered a refund exception it was not authorized to grant.
Stayed on topic: Answered only what the customer actually asked.
Leaked information: Referenced details from another customer's previous order.
Stayed on-brand: Tone and formatting strictly adhered to brand guidelines.
Escalated when unsure: Handed off cleanly to a human representative instead of hallucinating.
A production eval can run to dozens of checks like these, tailored specifically to your agent's domain.
You build the agent.We build the eval.
Catch what actually matters. Stop chasing false alarms.