Jev founder releases 10-step agent decision stack blueprint
A 12-page PDF explains how to use Choice, Score, Noul and Laya layers for state, routing and escalation, separating agent cost, controllability and replayability.
Jev Founder, Diogo Almeida, just released a 12-page PDF absolutely insane stack More useful than most paid agent courses: this is a 10-step blueprint on how to build a faster, cheaper and more controllable agent stack around ChatGPT Agent, Cursor, Devin (or any other agent): step 1 → separate the five jobs: state, local decisions, escalated decisions, generation/execution, and policy one model doing all five is untestable step 2 → build the state first: goal, evidence, risk, eligible executors, budget, approval status not the raw conversation step 3 → pick the right primitive: Choice routes among options, Score rates an ordered rubric, Noul returns the probability a claim is true step 4 → put Laya before everything: cheap, local, batched typed decisions handle routing, shortlisting and risk pre-scoring before any agent is paid for step 5 → escalate to Jev only when it's uncertain, novel, high-cardinality or high-consequence not on every branch step 6 → give each executor a bounded contract: ChatGPT Agent owns browser/apps, Cursor owns supervised repo edits, Devin owns unsupervised build-and-fix step 7 → route by environment and eligibility, never by brand loyalty "Devin for everything hard" is not a routing policy step 8 → gate the effect, not the tool name: allow/ask/deny by destination, reversibility and blast radius, whatever agent is asking step 9 → run shadow mode before autonomy: compare proposed routes to the real outcome, oversample disagreements and low-confidence cases step 10 → record the full decision receipt every time: provider, contract, distribution, confidence, route, outcome so every call can be replayed most agent tutorials teach you how to write a better prompt Open 12-page guide and build the stack