When the Model Decides Instead of Writes | Harness Blog

Harness describes TypeSafe’s Jev as a decision-focused AI model that returns typed Choices, Scores, or yes/no probabilities rather than prose or JSON that software must interpret, making agent judgments visible, storable, thresholdable, and auditable. Trained for calibrated decisions, Jev is intended to expose key forks in agent loops, such as selecting tools, deciding whether evidence is sufficient, routing work to a cheaper or stronger model, and assessing action risk, while policy code enforces how those decisions are used. Harness tested the approach in evaluation metrics, model routing, and safeguards for destructive actions, finding substantially lower latency than an LLM judge but mixed accuracy results, and showing that routing decisions can improve outcomes yet still raise total costs when longer agent loops offset cheaper model choices. The article emphasizes measuring decision quality separately from task outcomes, cost, and latency, and recommends retaining hard controls such as RBAC, policy enforcement, approval gates, and freeze windows while using probabilistic judgments to adjust autonomy and friction within those limits.

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