Hi everyone,
I have been building AI powered tools for a while now, including agents, automation systems, and content pipelines where structured output matters a lot. During this journey, one issue keeps showing up more than anything else: getting AI models to produce clean and reliable structure.
We all use JSON because it is simple and universal. It works perfectly for traditional software. APIs, config files, databases, JSON handles them all. But the moment an AI model tries to generate JSON on its own, things start to break.
Not because JSON is outdated, but because it was never built for the way AI produces information.
Let’s break that down.
Why JSON Struggles With AI Generated Output
JSON is strict. Every comma, quote, and bracket must be in the correct place for the output to work. Machines like this level of precision. AI models do not operate this way. They generate text in a flowing, natural manner that is sometimes too flexible for rigid syntax.
This leads to issues such as:
• One missing comma breaking your entire workflow
• The model mixing explanations with data fields
• A complex workflow turning into a wall of brackets
The core issue is simple. JSON forces AI into a rigid structure, while AI thinks step by step and adjusts along the way.
The Token Problem: Why JSON Makes Things Worse
Token usage plays a much bigger role than people realize.
AI models work within a limited context window. Every symbol counts as a token. JSON adds a lot of extra tokens because of:
• repeated brackets
• long field names
• quotes around every value
• deep nesting in structured data
When the model gets close to the token limit, it becomes more likely to:
• forget closing brackets
• shorten fields randomly
• merge fields together
• drop necessary structure
This is one of the hidden reasons why AI generated JSON often breaks at longer lengths.
TOON reduces this problem because it removes unnecessary symbols. It uses plain structure instead of heavy syntax. This means fewer tokens are wasted on formatting and more tokens are available for actual reasoning.
This small difference leads to a big improvement in stability.
TOON: A Format That Fits the Way AI Thinks
This is where TOON, known as Task Oriented Output Notation, makes a noticeable difference.
TOON is not a strict standard. It is a structure that mirrors the way AI naturally reasons. Instead of fighting the model with delicate syntax rules, it gives the model a clean path to express steps, goals, and decisions.
TOON supports:
• clear goals
• tasks broken into smaller actions
• stepwise reasoning
• explanations
• final results
The structure stays simple. The model does not lose an entire workflow because of one bracket or stray quote.
TOON works with the natural flow of AI instead of forcing it into a format that punishes small mistakes.
Where TOON Makes a Real Difference
In my own projects, especially agent based automations and multi step systems, TOON has improved clarity and reliability.
It works especially well when:
• the AI needs to think before acting
• the output has multiple sections
• a workflow involves several tools
• reasoning needs to be visible
• token limits are tight and JSON becomes risky
Any time the model needs clarity, structure, or longer reasoning, TOON makes the process smoother and more predictable.
Where JSON Still Matters
TOON is not here to replace JSON. It is here to complement it.
JSON is still the best choice for:
• APIs
• data storage
• configuration files
• strict schemas
• machine to machine communication
Use JSON when precision is required.
Use TOON when AI needs to think and explain.
A Simple Way to Understand It
JSON is built for machines.
TOON is built for AI.
They both matter. They simply serve different purposes.
If you have ever spent time fixing broken AI generated JSON or dealing with outputs cut off because of token limits, TOON will feel like a much more natural and reliable solution.
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