Pulling the model out of the agent decision layer cuts monthly cost from $400K to $1,200
The author describes a Jev Engineering architecture where a Gatekeeper, Router and Score/Block/Approve management layer handles decisions, calling the expensive model only when real reasoning is needed, for +19,000% speed and -99.7% cost.
A GUY WAS SPENDING $400K A MONTH JUST TO ASK "WHICH WORKER GOES NEXT." HE CUT IT TO $1200. not a typo. every "route this," every "is this done," every "should we retry" was going through a full frontier model, at frontier prices, for a call that takes milliseconds to actually decide. he didn't shrink the model. he pulled it out of the decision layer entirely. Speed: +19,000% Costs: -99.7% Jev Engineering turns a pile of AI agents into a real management system - and cuts the heavy, expensive model out of every decision loop. how the architecture works: Gatekeeper Request Mini-Model Validation Tool Router State Worker the management layer never writes a paragraph. it directs, evaluates, blocks, or permits. Route → Score → Block → Approve the expensive model only gets called when the task actually needs one. that's the whole idea behind Jev Engineering: decouple reasoning from deciding, and make the deciding layer fast, cheap, and something you can actually measure. detailed instructions in the article below ↓
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