Jev's routing layer removes expensive model calls from agent workflows
A diagram shows how Jev replaces repeated LLM calls with structured state, routing, validation and execution gates, which its tests measured as up to 193x faster and 444x cheaper.
this map shows every point where an agent system can avoid calling the expensive model most stacks send each decision back through the LLM: parse the request, choose a worker, inspect the result, approve the tool call, repeat. the same model ends up routing, judging and doing the work, so every branch adds latency and cost Jev Engineering moves that control logic into a measurable decision layer: request → structured state → Jev router → cheapest capable model → worker → validation → execution gate → tool those middle steps score requests, reject bad routes, approve valid ones and leave a trace you can inspect in Jev’s tests, this architecture ran up to 193x faster and 444x cheaper. the gains came from skipping model calls whenever defined software could make the decision the expensive model only enters when the request is ambiguous enough to need it the article below explains the router, gates and measurement layer in detail ↓
This post quotes another post: “Jev could become the control layer AI agents have been missing”