Using Jev as the routing and scoring layer in a coding workflow
The expensive model only writes code while Jev uses Choice/Score to pick the next worker, score drafts and decide on handoffs, escalating to a human when confidence falls below 0.85.
if your best coding model also decides which worker goes next, you hired a senior engineer to run a traffic light. that’s the JEV use case I care about. Give a backend workflow the repo, requirements and examples of the existing code. The expensive writing model builds the implementation. But choosing the next worker, scoring a draft and deciding whether a handoff needs review are different jobs. JEV takes the current state plus a narrow, typed question. It can return a choice, a score or a yes/no probability. Choice and Score also include confidence. Your code uses those answers to route the work: research → implementation → checks failed check → repair → checks again artifacts ready → human review The useful part is the evidence between steps. A changed file, the current revision and the latest test output go into the state. A test failure is a fact the runner can act on. JEV can judge the next narrow step; it doesn’t turn its opinion into proof that the code works. If the requirements are ambiguous, the workflow should expose that gap. In this replay, a Choice confidence floor of 0.85 routes uncertainty to a human. That’s an example policy, not an 85% correctness guarantee. The reviewer gets the implementation, tests, docs and summary. Merge and deployment remain separate approvals. The economics are worth understanding: TypeSafe publishes $42 per billion input tokens, or $0.042 per million. At an average of 1,000 input tokens per call, 10,000 calls would mean about $0.42 in JEV input charges. That’s arithmetic, not a measured run or the cost of the whole coding system; the writing model and tools still have their own costs. The attached video is a scripted HTML replay of this architecture. It shows the loop worth testing: expensive models create, JEV supplies narrow judgments, code enforces the workflow, and a human owns the final handoff. That’s the split I’m breaking down in my JEV article. Before adding another agent, decide which decisions actually need your best model.
This post quotes another post: x.com/0xkurt/status/2103410921892618651