Jev for AI Agents: How System One Models Decide

Course Overview

This course explains the decision layer inside AI agents, focusing on System One models that provide fast, typed answers without parse failures or invented fields. It uses Jev as a worked example to show how these models route decisions, manage latency and cost, and handle failure modes. The course is an explainer without exercises or coding, designed to give a deep understanding of how agentic AI decisions are made and evaluated.

Key Takeaways

• Understand the nature of System One model outputs and their reliability.
• Learn how multiple questions are answered in parallel to optimize latency and cost.
• Distinguish between typed decisions and LLM calls, and between probability, confidence, and truth.
• Gain insight into confidence-gated routing and how thresholds are set based on action costs.
• Analyze a real agent rebuild around typed decisions, including its successes and failure modes.
• Identify common failure modes such as confident errors, miscounting, and inconsistent answers.
• Understand the public debate on calibration and how to interpret vendor benchmarks.
• Evaluate the cost-benefit of decision layers and know which decisions should remain in code.

Prerequisites

• No prior experience required.
• Ability to read code is necessary; no coding is required.
• Some familiarity with AI agents is helpful but not mandatory.
• Interest in understanding mechanisms rather than following coding tutorials.

Target Learners

• Developers wanting to understand AI agent decision processes beyond prompt reliance.
• Tech leads responsible for latency budgets and cost management in design reviews.
• Engineers familiar with agent stacks seeking knowledge of the decision layer.
• Testers, security engineers, product managers, and compliance specialists involved in agent decisions.
• Anyone needing to explain agent loop costs and decision mechanisms.
• Not suitable for learners seeking hands-on build-along courses or framework tours.