Six steps to cut agent costs 100x by replacing the decision layer with Jev
The guide audits LLM calls to separate writing from deciding, lets Gemini write and Jev decide while code acts, and uses Choice/Score/Null with rubrics and batched gating.
Jev Engineering cuts AI agent costs by 100x on Google's stack your agent spends 90% of its budget deciding, not thinking. Jev makes those decisions for almost nothing here's how to rebuild your agent's brain in 6 steps: step 1 → audit every LLM call. log each one and tag it: "writes" (drafts, summaries, code) or "decides" (routing, scoring, yes/no). most agents run 70%+ decisions step 2 → split the architecture. Gemini writes. Jev decides. code acts. each job goes to the component built for it step 3 → structure your decisions. Jev takes three types - Choice (pick from options), Score (rate on a scale), Noul (yes or no as probability). no open-ended prompts step 4 → build rubrics, not prompts. every decision gets a JSON rubric with definitions, boundaries, and examples. version-controlled, testable, no hallucinated options step 5 → batch and gate. send all questions in one call (12x cheaper than sequential). set confidence thresholds - high confidence auto-acts, low confidence escalates to Gemini step 6 → measure cost per completed task, not cost per call. a cheaper decision that breaks the workflow costs more than the expensive one that worked the math: 10,000 daily decisions - $35,000/month on frontier models. $120/month on Jev. same accuracy, 193x faster bookmark this. your agent bill will thank you ↓
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