---
title: "Discussion on \"Who Evaluates the Evaluator? Testing AI Simulators in Production\" | Hashnode"
description: "Discussion on \"Who Evaluates the Evaluator? Testing AI Simulators in Production\". In my last post, I wrote about using simulators to evaluate multi-turn AI agents. It is a powerful technique, but things can get confusing fast if we do not clearly define the testing architecture.\nIn"
url: "https://hashnode.com/posts/who-evaluates-the-evaluator-testing-ai-simulators-in-production/6ad4da90529ef7522795cef0"
---

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# [Who Evaluates the Evaluator? Testing AI Simulators in Production](https://mundher.com/who-evaluates-the-evaluator-testing-ai-simulators-in-production?utm_source=hashnode&utm_medium=feed)

In my last post, I wrote about using simulators to evaluate multi-turn AI agents. It is a powerful technique, but things can get confusing fast if we do not clearly define the testing architecture. In

Oct 18 · 3 min read · mundher.com  
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## Written by

[Mundher Al-Shabi, PhD](https://hashnode.com/@Mundher)

@Mundher

  
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I design and build machine learning models and systems.

## Responses 1

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[BomToBe](https://hashnode.com/@bomtobe-orke)

[Oct 18](https://hashnode.com/posts/who-evaluates-the-evaluator-testing-ai-simulators-in-production/6ad4da90529ef7522795cef0/comment/6ad4e0da9ee133b5f8149c3f)

One useful validation is to check the judge metrics against blinded human ratings on a held-out set, and report agreement by failure type. A discriminator can separate human and synthetic transcripts by stylistic artifacts without proving the simulator followed its scenario; a plausible exchange can still violate the scenario contract. Keeping naturalness and behavioral fidelity as separate scores makes that gap visible. I work at BOMTOBE, the team behind Orke; this is a design suggestion, not a test I have run.