Jev 接管工具调用循环：12/12 通过、成本降 36%
把工具选择与下一步决策交给 Jev，主 LLM 只负责框定任务和总结，12 项工具调用基准全过，外层 token 少 89%。
I used Jev to delegate the entire tool call, execution, and next-step loop away from the main LLM. The setup was simple: On one hand we have the native vanilla Harness . The LLM sees the tool catalog, picked a tool, got the result, reasoned again, picked the next tool, and repeated. The other harness gave the LLM exactly one tool: `delegate_to_jev`. This acts as the starting state. Jev then handled the closed-set routing: which real tool to call next, what typed arguments to use, when to continue, and when to stop. The LLM only framed the task and summarized the final evidence. We ran this on a deterministic tool calling benchmark with 12 tasks: multi-step workflows, safety checks, prerequisite chains, and state-changing actions. Results: Baseline harness: - 10 / 12 scenarios passed - $0.01966 cohort cost - 49,364 outer-LLM tokens LLM + Jev delegation: - 12 / 12 scenarios passed - $0.01249 cohort cost - 5,363 outer-LLM tokens That is ~36.5% lower cost and ~89.1% fewer outer-LLM tokens. That is crazyyyyy!! This means, we can use the LLM for reasoning and synthesis. And use Jev for fast, typed, repeated decisions inside the tool loop.