Jev 发布 10 步 Agent 循环省钱蓝图
一份 10 步方案：把 LLM 写作、代码硬规则和 Jev 决策分层，只在必要时调用 Grok Bot，从而压低前沿模型成本。
holy sh*t. Jev engineers just dropped a PDF on how to run agent loops without paying frontier-model prices at every fork this is a 10-step blueprint for turning one messy LLM workflow into a cheaper, tighter and far more controllable agent system: step 1 → split the stack into 3 layers: the LLM writes, code owns hard rules, and Jev handles the bounded decisions in between step 2 → build compact state first: request, evidence, permissions, limits and only the context needed for the next move step 3 → start with a narrow gate: is this task valid, useful and safe enough to continue, or should it stop or go human step 4 → route with typed choices: ignore, log, draft or human instead of asking a frontier model to reason through every event step 5 → keep budgets, permissions and destructive-action checks in code so the model can never invent its own boundaries step 6 → call Grok Bot only when the selected route genuinely needs text, planning, summarization or open-ended reasoning step 7 → check evidence before drafting. if the facts are missing, escalate instead of letting the writer guess step 8 → score what comes back: keep, retry, summarize or drop, while logging the decision and its confidence step 9 → audit the silent lane too. ignored events need sampling because that’s where invisible mistakes can hide step 10 → track the numbers that actually matter: cost per completed task, latency, escalation rate and how often human review was justified the useful shift here isn’t “find a smarter model.” it’s making the expensive model one worker inside the system instead of letting it become the entire system. Jev decides, Grok Bot writes, code keeps control.
这条是引用着另一条写的：x.com/gippp69/status/2103394701063737504