Jev 团队发布 Agent 省 LLM 调用实战指南

fleyta @fleyta88原推发布于 2026.09.29

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Jev engineers just published a field guide for building agent systems that stop wasting LLM calls the useful part isn’t another prompt trick. it’s a control stack where Jev handles the small decisions, Grok Bot writes only when needed, and code keeps authority over everything irreversible. here’s the 10-step setup: step 1 → split the system into 3 layers: Jev decides, Grok Bot writes, and deterministic code owns permissions, limits and execution step 2 → turn every event into compact state: request, evidence, policy and only the context needed for the next decision step 3 → start with a narrow gate: decide whether the event should be ignored, logged, drafted or sent to a human step 4 → separate intent, urgency and evidence into individual typed questions instead of one giant evaluator prompt step 5 → route before generation so low-value events never touch the writing model in the first place step 6 → give Grok Bot a bounded job only after the route is known, with the exact context that branch needs step 7 → keep risky actions outside the model: money, permissions, external changes and destructive steps stay behind code or human review step 8 → send low-confidence or conflicting decisions upward instead of letting the system quietly guess step 9 → log every route and audit a sample of ignored events, because silent mistakes are the easiest ones to miss step 10 → measure the economics: at 20,000 events, Jev can cost about $0.84 while just 4% escalating into writing adds another $24 that’s the part worth stealing. the biggest saving doesn’t come from finding a cheaper writer. it comes from making sure the writer is only called when the task actually deserves one.

这条是引用着另一条写的：x.com/gippp69/status/2103394701063737504