Technical Handbook Breaks Down Decision Models for AI Agents via Drex 2.0
techNmak · x · 2026-10-20
A technical handbook by @techNmak makes the case for decision models: instead of free-text generation, agents evaluate defined outcomes with probabilities over typed questions, useful for classification, tool selection, routing and severity assessment.
Key points:
- Interface: the app supplies a state, a question and candidate answers; the model assigns probabilities rather than generating text.
- Three output contracts: binary (compare to threshold), categorical (mass over alternatives), and ordinal (expected score over ordered levels) — each needs different application-side handling.
- Limitations: softmax normalization doesn't guarantee calibration; high probability ≠ correct; changing the option set changes the problem, and omitting the right answer forces probability mass onto wrong alternatives; actions must account for error costs, thresholds, permissions and escalation policies.
- Implementation study: uses Nace AI's open-weight Drex 2.0 to dissect candidate representation, pointer-head scoring over hidden-state readouts, logits-to-probability conversion, and shared-state inference; Drex Max extends the typed interface to images, with new issues around preprocessing, context limits and visual ambiguity.
- The handbook spans first principles through temperature scaling, calibration, evaluation and production failure modes, with worked math examples.
Related event: Decision Models: AI Agents Output Decision Probabilities Instead of Text(2 posts)→
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