Using Jev as a cheap decision layer for agents: 10,000 decisions for 42 cents
A single diagram lays out the architecture of Claude thinking, Jev choosing, and code executing, with routing, scoring, and approvals handed to Jev, covering choice/score/Noul question types, Browser Use menu refreshes, and a real cost breakdown.
JEV + YOUR AI AGENT = 10,000 DECISIONS FOR 42 CENTS This one sheet maps the whole setup. From an empty folder to a working AI chief of staff with one API key. Right now your agent calls a frontier model every time it has to pick a worker or approve a draft. Jev answers those calls for a fraction of a cent. The split: Claude thinks, Jev picks, your code pulls the trigger. The setup: 1. Spot the cheap decisions > Routing, scoring, approvals. Anything with a fixed menu of answers goes to Jev. 2. Try it with zero code > One state, one choice question, three options in the playground. 3. Build the router > chief. py, about 40 lines. Below 0.85 confidence the job lands in review. 4. Pick the right question type > choice for "which one", score for "how good", Noul for "yes or no". 5. Refresh the options > Browser Use rebuilds its menu after every click. Its agent finds flights in 7 seconds. 6. Fire questions together > Same models, browser calls down from 1,092 to 101. 7. Read the real price > $0.042 per 1M input tokens, zero for output. 10,000 decisions come to $0.42. 8. Ship a use case > Flights for $0.0039, 1,018 papers for $0.08, 500 emails for 3.5 cents. Your agent stops paying frontier prices for a coin flip, and the big model only wakes up for work that needs it. Put this in one agent this week. Next month's AI invoice will show the difference. The full guide with working code is in the article below ↓
This post quotes another post: “Long read: how to build a Jev-powered agent stack”