System One decision models reshape custom agent harnesses as CLM hits 9x Jev speed
_AustinCalvert_ · x · 2026-09-26
A discussion sparked by DAIR.AI's Omar argues that a new wave of "System One" decision models changes the economics of building custom agent harnesses. These small models handle the harness's frequent micro-decisions — which model to route to, whether a tool call is safe, whether an answer is good enough — without a full LLM call each time.
Key points:
- After Jev (TypeSafe AI, trained with RLCD, returns typed decisions with probabilities), a new Contrastive Language Model (CLM) embeds situations and candidate actions and ranks by similarity — 9x faster than Jev and seemingly a better verifier on long-horizon tasks
- Why own your harness: control of your intelligence stack, System One + System Two combos beat either alone, richer proactive agent experiences, more value per token
- A DAIR.AI Academy lab shows how to build a custom harness on the Pi SDK using Jev for model routing, risky tool-call blocking, and answer checking
- Author expects frontier labs to push decision models further with better customization tooling
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