Jev founder's ten-step guide to agent architecture
A breakdown of Jev's layered architecture where Jev makes narrow decisions, code holds authority, and the LLM is called only when generation is needed, including a $0.84 vs $24 cost comparison at 20,000 events.
Jev Founder Just Dropped a Field Guide for Building Agent Systems That Don’t Waste LLM Calls the interesting part isn’t another “better prompt” trick. it’s a different architecture: Jev makes the narrow decisions, code keeps authority, and the LLM only gets called when open-ended generation is actually needed. here’s the setup i’d steal from it: step 1 → split the system into 3 jobs: Jev decides, the LLM writes/reasons, deterministic code controls what can actually happen step 2 → turn raw events into compact state: request, evidence, policy and only the context needed for that decision step 3 → use typed decisions instead of giant evaluator prompts: Choice for routes, Score for ordered judgments, Noul for yes/no probability step 4 → separate intent, urgency, evidence and risk into independent questions instead of asking one model to figure everything out step 5 → route before generation: ignore noise, log useful events, draft only when text is required, escalate anything sensitive or ambiguous step 6 → give the LLM a bounded job after the route is known, with only the files, tools and context that branch needs step 7 → keep code as the authority layer: apply rules, update state, call tools and block anything outside policy step 8 → treat low confidence as routing information, not proof that the answer is wrong. uncertain or conflicting cases move upward to review step 9 → log every decision and sample the silent lanes. the dangerous mistakes are the ones nobody ever sees because they were ignored step 10 → measure the actual economics: at 20,000 events, Jev can cost about $0.84 while just 4% of events pushed into writing add another $24 that last number is the real takeaway for me. the important optimization isn’t finding the cheapest LLM. it’s reducing how often you need an LLM at all. once routing, confidence and guardrails become separate from generation, the agent stops behaving like one giant prompt and starts looking more like an operating system.
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