Stop Debugging Black Boxes: How jac-agent Solves the 3 Hardest Pain Points in Training Production-Grade AI Agents

Subtitle If your training data is messy, your logs are useless, and you can’t prove why your agent failed — this is for you. Intro Training AI agents isn’t just about prompt engineering anymore. If you’re building anything that touches production, you’re already hitting these walls: You can’t trace failures. A bad decision derailed your pipeline — but you have no idea which step caused it. Your training data is garbage. You’re scraping unstructured logs to build SFT/RL datasets, wasting hours cleaning noise. You can’t deploy safely. Regulators and auditors want proof your agent isn’t making harmful choices, but you have no way to show it. Today, I’m releasing jac-agent : an open-source SDK built on IETF standards, designed to solve exactly these problems — while adding zero overhead to your training loop. GitHub: github.com/hjs-spec/jac-agent The 3 Pain Points of Training Production Agents 1. The "Black Box" Debugging Nightmare You run an agent for 100 steps. It makes 99 good decisions