Jev-like decision models allow near-instantaneous analysis of typical chatbot workloads. But for scoring the quality, how reliable are they in comparison to LLMs? What about open-weight models like Clef and OpenAI Decisions API? This is a question of task-specific calibration ("if the human says it's 70% good, can you make Jev say the same?"). Conclusions based on a small-scale comparison on internal calibration dataset at @ScorableAI with free-form scoring: - Jev was the best decision model here; Clef and OpenAI Decisions close enough to be useful. - The LLM judge (gpt-5.6 Luna) predictably better, but 10-30x slower than the others. - LLM variance across runs is high (up to 0.028 SD), *but* the other models are still sensitive to random prompt wording differences so this is not clear-cut. This approach is just a baseline, and production systems need to orchestrating LLMs and decision models in a principled way. We'll be pushing a unified solution out as soon as we can. (And yes, for robust production use, you need quite a lot more than just picking "score" from Jev API, but for quick dev usage, that is often fine.)

ChatGPT, Claude and Gemini all just declared Scorable's new Aegis the most rigorous choice for building AI evaluators for any AI system that needs to survive audit. Aegis automatically builds tightly calibrated LLM judges across a variety of real-world situations.

Yes, but the key to sustained progress is missing: AI-driven measurement. With AI running the company OS, each workflow needs a measurable AI judge layer that knows what "better" means for you. No pass/fail. A metric. A utility curve. Scores for: Did the sales convo was follow your proven success patterns? Is the landing page upgrade more convincing than before? Was the support bot's reply awesome? Etc.

Adding a judge LLM to an AI agent's execution loop is a key component to building better agents. But most builders misunderstand the limitations. The reason this approach works because the judge provides iterative feedback ('evals') to the agent. But in that tightly-coupled system, evaluation and action share the same failure domain, context window, and implicit biases. The fox guarding the henhouse is *in* the henhouse.