TypeSafe just released a Blueprint for building x200 and x400 cheaper Agentic Loops with Jev
I collected the best tips in a structured 14-page PDF on "How to ship an effective agent JEV LOOP":
step 1 → meet Jev: a System One model. It doesn't generate text. It takes state + questions and returns typed answers with probabilities
step 2 → learn the three question types: Choice picks one option, Score places it on a scale, Noul returns the probability something is true
step 3 → batch questions per state: every question runs in parallel in one call, so extra questions barely add latency
step 4 → split the loop: the LLM thinks and writes, tools act, Jev takes every bounded fork in between
step 5 → route models with Jev: fast model for lookups, powerful model for architecture, picked from the latest message
step 6 → guard every tool call: AutoModeMiddleware scores bash calls for risk and blocks them before they run
step 7 → replace LLM-as-judge: correct, grounded and complete scored in parallel on the same trace
step 8 → check the test: 5 frozen runs, 100 repeats per judge. Jev matched the human oracle on 500/500 decisions. Terra 99.8%, Luna 96.4%, Claude Sonnet 4.6 80%
step 9 → do the math: 0.44s and $0.00035 per call. $0.34 total vs $28.17 for Claude Sonnet 4.6, with 92-913x lower variance
step 10 → keep thresholds in code and a human in the loop: stable doesn't mean right. Claude gave the same wrong verdict every single time
the result: the expensive model only does the work that needs it, and every decision around it runs in under half a second
Send this PDF to your LLM before running your next agentic workflows, then explore how to become a Jev-native engineer in the article below
Sep 22, 2026 · 4:47 PM UTC
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Blueprint for Building Agentic Loops with Jev ↓
drive.google.com/file/d/1spq…
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