CHOOSEAgents & automation
JEV: Structured Output for AI Decisions
Jason Goldberg@betashop
JEV is an AI system that generates structured, typed outputs directly, eliminating the need for parsing or retries by bounding the output space before inference and accepting predefined answer types.
Pattern↑ Jev AI decision models
here's how jev works, simplified.
an hour in.
it is extremely good at making decisions when you give it the right references and context.
the actual shift: there's no decoder loop. it isn't sampling tokens one at a time and hoping the last one closes your json. the output space is bounded before inference starts — you declare the answer type, it scores over that set, done.
you hand it three things:
> the information
> your questions
> the shape of each answer — a choice from your options, a score on your scale, a probability that something is true
what comes back is already typed. no parse step. no retry when it wraps the json in a code fence. no schema drift on the third call.
information + questions + allowed answers → a value your code branches on
the rest is yours.
> route it
>> check an agent's work
>>>flag it for a human.
the hallucination thing is a side effect of the architecture, not a safety feature. there's no paragraph, so there's nowhere to invent a citation. it can still be wrong. it just can't be wrong in prose.
and the cost profile changes what you're willing to ask. at a fraction of a cent and sub-second, a decision stops being something you batch overnight and becomes something you put in the hot path.
for users with quant agents, we point it at two:
1️⃣ every user interaction. stuck or not, which layer it broke on, does it need a human.
2️⃣ every trade. size within the rule, stop present, same mistake as last tuesday.
both are decisions, not essays. neither needs a paragraph.
➡️ take one decision your app already makes, declare its answer type, hand it over.