Jev AI Crash Course: System 1 Decision Models for AI Agents

Course Overview

This course provides a comprehensive guide to Jev AI and System 1 AI Decision Models, focusing on building ultra-low-latency AI agents using Jev and TypeSafe AI. It covers decision models, tool-gating, rubrics, triage, and the integration of fast decision models with generative LLMs for efficient AI workflows. Students will learn to implement cost-effective, real-time decision pipelines and agent safety guardrails.

Key Takeaways

• Understand when to use fast System One decision models versus heavy System Two generative LLMs.
• Master TypeSafe AI’s core primitives: Noul (probability), Score (rubrics), and Choice (classification).
• Build ultra-low-latency (sub-100ms), cost-effective decision pipelines for real-time AI systems.
• Implement agent safety guardrails and tool-gating to intercept and block risky actions in real time.
• Extract deterministic, typed JSON evaluations without LLM text bloat or hallucinations.
• Learn integration of decision models with LangChain and local Ollama backends.
• Optimize AI agent costs at scale using decision model router middleware.

Prerequisites

• Basic understanding of programming (Python, JavaScript, or any language interacting with REST APIs and JSON).
• Basic familiarity with AI concepts (prompts, LLMs, or AI agents) is helpful but no advanced machine learning or math background is required.
• A computer with internet access to use the TypeSafe AI console and APIs.

Target Learners

• AI Engineers & Backend Developers building autonomous agents, workflow automations, or high-volume data pipelines.
• Software Developers needing ultra-low-latency classifications, support ticket triage, or bug severity scoring.
• Engineers building Agent Guardrails to reliably gate dangerous tool calls in coding agents.
• Teams aiming to reduce AI costs by replacing expensive generative models with lightweight, sub-cent decision models for non-generative tasks.