Pulling decisions out of LLM calls: Jev's decision layer claims a 193x speedup
The author argues agent stacks are missing a layer of LLM reasoning, Jev deciding, and tool execution, so handing state and action choice to Jev can be benchmarked separately, with self-reported results of up to 193x faster and 444x cheaper.
Stop tuning the model. The 193x speedup lives one layer up The layer that's missing from most agent stacks now has a name. Jev Engineering. It handles everything that happens between your model calls. Every agent eventually hits the same fork: > State > Multiple possible actions > One route has to win Most stacks send that decision back to an expensive LLM call. Jev turns that decision into a dedicated layer. The loop becomes: > LLM reasons > Jev decides > Tools and agents execute > State updates > Jev decides again Once decisions become their own engineering layer, you can benchmark them, batch them, and verify them separately from the model. Reported result on their own tests: up to 193x faster, 444x cheaper. The model race is late-stage. The decision-layer race is early. Full blueprint in the article below
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