Jev as an agent decision layer: 193x faster and 444x cheaper
The author tested replacing LLM text generation for tool routing with Jev's structured decision layer, claiming 193x lower latency and 444x lower cost, and outlines a new loop of LLM reasoning, Jev deciding, and tool execution.
THE $0.042 SECRET THAT MAKES JSON-BEGGING LOOK LIKE A JOKE Jev Engineering is the layer most agent stacks are entirely missing. Every production agent eventually hits the same fork: state → multiple possible actions → one route has to win Right now, you are probably renting a poet to do a bouncer's job. You're forcing an expensive LLM to generate text, token-by-token, just to pick a tool. It's slow, it hallucinates schemas, and it burns through your API budget. Instead of sending every routing decision back to a bloated text model, top builders turn that into a dedicated decision layer. The results from our tests: - Up to 193x faster - 444x cheaper - Type errors become mathematically impossible Everyone is obsessing over optimizing the main model. Jev Engineering optimizes what happens between model calls. The new, high-speed agent loop looks like this: LLM -> reason Jev -> decide Tools / Agents -> execute State -> update Jev -> decide again Once decisions become their own sub-second, perfectly typed engineering layer, you stop rationing intelligence. You can finally benchmark, batch, and verify them separately. Full blueprint in the article below ↓
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