Why JSON Breaks in AI Pipelines — and the TOON Format I Built to Fix It

Why JSON Breaks in AI Pipelines — and the TOON Format I Built to Fix It JSON has been the default data format for decades. It’s simple, readable, and works almost everywhere. But recently, while building AI-driven workflows, I kept running into the same problem: 👉 JSON isn’t designed for meaning . It’s designed for structure . The Problem I Hit When working with LLMs and agent-based systems, I noticed: JSON is too verbose for iterative reasoning Nested structures become hard to interpret semantically Relationships between data are implicit, not explicit Small changes break parsing or require rigid schemas Example { "task" : { "name" : "generateReport" , "input" : { "user" : "Alex" , "role" : "developer" } } } This works for machines — but for AI systems trying to reason , it’s noisy. What I Needed Instead I wasn’t looking for a “better JSON”. I needed something that: Is human-readable at a glance Represents relationships clearly Works well with AI parsing + generation Reduces unnecessa