CHOOSEAgents & automation
Jev: Simplified AI Decision-Making
Termsheetinator@termsheetinator
Jev is an AI mechanism designed to rapidly and efficiently score choices and select the most likely correct one, simplifying complex decision-making processes for AI agents.
TakeJev is built to just score the choices and pick one.
Pattern↑ Typed decisions for agent routing and selection
Everyone is hyping up Jev and somehow making it sound 10x more complicated than it is.
Here’s Jev for dummies:
Jev basically does one thing extremely well, extremely fast for extremely cheap..
Your harness gives it information + choices.
It tells your harness which choice is most likely correct.
That’s it.
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Normal LLM routing a support ticket:
Ticket:
“Hey, I was charged twice this month. Can someone fix this?”
The LLM has to work through something like:
“The customer mentions being charged twice.
That sounds payment-related.
Could this be general support? Maybe.
Could it be account management? Probably not.
Billing handles invoices, charges and refunds.
So billing is most likely the correct department.”
Then:
Answer: Billing
That whole process is overkill when the only choices are:
> Sales
> Billing
> Technical Support
> Account Management
> Cancellations
Jev is basically:
Ticket: “I was charged twice this month.”
And immediately scores the options:
> Billing: 97%
> Technical Support: 1%
> Account Management: 1%
> Cancellations: 0.7%
> Sales: 0.3%
> Route to Billing.
LLMs can spend time “thinking” their way to a choice.
Jev is built to just score the choices and pick one.
When you have an AI agent doing that thousands or millions of times, that’s where it starts making a ton of sense.
Think:
“Is this company an ICP?”
Yes / No
“What type of reply is this?”
Interested / Objection / OOO / Unsubscribe
“Which campaign should this lead go into?”
Campaign A / B / C
“Which tool should the agent use next?”
Tool A / B / C
“Is this task finished?”
Yes / No
“Does this need a smarter model?”
Yes / No
It’s really good for:
> classification
> routing
> scoring
> ranking
> yes/no decisions
> choosing between known actions
> validating information
> deciding what an agent should do next
Basically:
Codex = does the work.
Jev = helps Codex make a shit ton of tiny decisions while doing the work.
Now install it.
Sign up for the waiting list, I got my invite link within ~20hrs
Install the official TypeSafe skill:
npx skills add typesafe-ai/skills --skill typesafe-ai
Select Codex when it asks which agent.
Want it globally..
npx skills add typesafe-ai/skills --skill typesafe-ai -g
Add your API key:
TYPESAFE_API_KEY=your_key
Tell Codex once:
“Use the TypeSafe skill to find places in this project where Jev should replace brittle rules, parsing, classification, routing, scoring or closed-set decisions.”
If you want Codex thinking about Jev automatically, add this to your AGENTS.md:
“Use TypeSafe/Jev by default whenever a task involves classification, routing, ranking, scoring, validation, or choosing between known actions. Prefer Jev over a generative LLM for closed-set decisions.”
Now you don’t need to type:
“USE JEV”
every 5 seconds.
The skill teaches Codex how TypeSafe works, its question types, patterns and best practices.
And your AGENTS.md tells Codex when you want it applied.
That’s it, simple.
