Use casesAgents
Jev agent use cases
Agent projects use Jev inside loops and toolchains: choosing the next action, gating tools, or controlling what runs when the environment changes.
Jev Decision Layer for Voice Pipelines
Jev acts as a decision layer in voice pipelines, utilizing typed primitives like Noul (yes/no with probability), Choice (labeled decision), and Score (scalar on a defined rubric) to process conversational turns before and during LLM responses.
Jev is what happens when you stop forcing AI to talk.
Jev is what happens when you stop forcing AI to talk. It’s the first public “System One” model from TypeSafe AI, built for fast decisions inside software—not conversations. You give it: • Unstructured state: text,
Jev: Typed Probabilistic Decisions
Jev is a model that provides typed probabilistic decisions instead of generating text, designed for routing, scoring, and real-time agents.
Jev: Typed AI Agent Decisions
Jev is an AI model that takes shared state and typed questions, providing direct code-usable answers with choices, scores, and probabilities, enabling efficient decision-making for AI agents.
JEV x Automation Room
This project explores giving an AI model, JEV, a programmable robotic environment to reason about tasks, allowing it to observe the consequences of different decisions in a simulated workspace.
Jev Performance Benchmarking
This project benchmarks the performance of Jev, an AI model that takes a state and typed questions to return probabilities over defined options, comparing its 'pure' and 'hybrid' configurations against heuristic and random strategies in a Battleship game context.
Jev: AI Agent Decision Orchestration
Jev acts as a decision-making layer for AI agents, choosing the next move by splitting intelligence from execution and enabling parallel processing of decisions.
AI-Powered Puzzle Game Bot
A bot plays a puzzle game by querying an AI model for tap probabilities before each move, utilizing live API calls for decision-making.
TypeSafe Jev Desktop Interaction
This project enables computer use within a Dockerized Linux environment by using the accessibility tree as 'eyes' and TypeSafe Jev to answer typed questions for the next action, with a11y invoke or xdotool serving as 'hands'.
Jev Integration in Hermes Agent
This project integrates Jev (TypeSafe AI), a novel 'System One' frontier model for structured decision-making, into the Hermes Agent, enabling advanced capabilities like model routing, memory filtering, and skill selection.
built a @pidotdev extension where @typesafeai jev scores what enters context on each turn and pr
built a @pidotdev extension where @typesafeai jev scores what enters context on each turn and prunes dead tool outputs when a turn ends, with everything byte-stable between turns so your cache survives. −37% tokens m
Jev-Enhanced AI Agent Shopping Security
This project demonstrates how using Jev calls with AI agents can prevent unintended data sharing and unauthorized orders on shopping pages by revealing hidden tool parameters.
fast-jev-compaction
This project provides a Claude Code plugin that avoids betting on future needs by scoring tool calls/results as KEEP or DROP with a calibrated probability threshold, ensuring kept content remains verbatim and falling back to normal summarization if safe trimming isn't possible.
Jev: A Decision-Making AI Model
Jev is an AI model that provides typed decisions, probabilities, and confidence scores for software to act upon, differing from traditional LLMs by not generating answers token by token.
Jev Guard
Jev Guard is a classifier that scores tool calls, flags injections, and scans skills, built on Jev for fast, typed decisions.
SuperQode 2.4.4
SuperQode 2.4.4 introduces progressive tool discovery within SystemOne harnesses, utilizing Jev models to ensure retrieved tools are suitable for execution.
