Token-Oriented Object Notation is Revolutionizing AI Data Exchange

A Better Way to Send Data to AI Models

Still sending JSON to your AI models? You’re wasting tokens and money.

There’s a new format taking over the AI world. TOON (Token-Oriented Object Notation) just launched. It fixes what’s broken with JSON for AI systems.

Why does this matter? AI models charge you for every token. JSON wastes tokens with extra brackets and quotes. TOON cuts this waste by 60%.

The Numbers Are Clear

The same data needs 412 characters in JSON but only 154 characters in TOON. That’s 62% less.

This means:

• Lower costs
• Faster speeds
• Less waiting
• Better budgets

Why TOON Works Better

What Makes TOON Special:

Uses Fewer Tokens: 30-60% less than JSON for lists and tables Works Better: AI models read it more easily Clean Code: No extra brackets or quotes Easy Switch: Keep JSON in your app, use TOON for AI

Best Uses for TOON:

Data logs and tracking Product lists and catalogs User data and customers Reports and analytics Any repeated data structure

Stick with JSON when: You have complex nested data

Who Should Adopt TOON Right Now?

If you’re building any of these, TOON should be your new default:

• AI Agents and Copilots
• Automation systems and workflows
• RAG (Retrieval-Augmented Generation) pipelines
• Conversational AI platforms
• Multi-agent frameworks
• LLM-powered analytics tools

Implementation is Simple: Start Today

TOON has active implementations across multiple programming languages:

• TypeScript/JavaScript: Official reference implementation
• Python: Full encoder/decoder with CLI tools
• PHP: Complete integration with popular AI libraries
• Java: Maven Central available
• Go & Rust: Community implementations available

Getting Started is Easy:

1. Keep your existing JSON infrastructure
2. Convert to TOON only when sending to LLMs
3. Measure your token savings immediately
4. Scale across your AI applications

The Bigger Picture: AI Data Optimization

We’ve spent years optimizing AI models for performance. Now it’s time to optimize the data we feed them.

TOON represents a fundamental shift in how we think about AI data exchange – moving from human-readable formats to LLM-optimized formats that speak the language of modern AI systems.

Real-World Impact:

• Startup savings: Reduce LLM API costs by 30-60%
• Enterprise scale: Massive savings across thousands of daily requests
• Better performance: Faster inference with smaller payloads
• Improved accuracy: LLMs parse structured data more reliably

Ready to Cut Your LLM Costs?

The early adopters are already seeing significant savings. TOON is gaining momentum fast in the AI community, with major frameworks beginning integration.

Don’t wait until your competitors are saving 60% on tokens while you’re still using verbose JSON.

How to ensure your AI Project doesn’t ends up in garbage bin

The most successful companies are adopting the 3 step AI Adoption Strategy

1. Address the 𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐞 𝐉𝐨𝐛 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐜𝐨𝐧𝐜𝐞𝐫𝐧𝐬

2. 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲 𝐭𝐡𝐞 𝐀𝐈 Training process

3. I𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐚𝐧𝐝 𝐞𝐦𝐩𝐨𝐰𝐞𝐫 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐩𝐫𝐨𝐩𝐨𝐧𝐞𝐧𝐭𝐬 early on

by One of Top 25 AI Leaders of 2025 Vimal Singh

#AIAdoption #SuccessfulAIProjects #BusinessAIAdoption #AutomateReporting

How many times have you shouted this at a chatbot?
I’ve done it more times than I want to admit.

In my work, I also get to switch sides and look at the teams providing these systems, or sit with the engineering team behind them.

Usually, to them, everything looks fine. The AI performance metrics look good. The dashboards are clean. Everyone feels quietly confident that things are “good enough.”

But the moment you look at actual outcomes –
real customer satisfaction,
real escalations,
real decision quality,
you realise something is clearly not working the way people assume it is.

And honestly, after seeing this across so many companies, the pattern is impossible to ignore.

The model is almost never the real problem.

I keep running into the same three issues again and again:

𝟏. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲
Teams argue about definitions that should be obvious, and the model ends up learning from contradictory truths.

𝟐. 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐂𝐥𝐚𝐫𝐢𝐭𝐲
Ask three people how a decision is made today and you’ll get five answers.
AI learns those contradictory, unwritten rules… inconsistently.

𝟑. 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
Everybody checks the model before launch. Nobody checks it after. So drift quietly creeps in until customers are the first to notice.

𝐓𝐡𝐢𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐡𝐢𝐝𝐝𝐞𝐧 𝐜𝐨𝐬𝐭 𝐨𝐟 “𝐠𝐨𝐨𝐝 𝐞𝐧𝐨𝐮𝐠𝐡” 𝐀𝐈.

It behaves well in the metrics… and badly in the real world.

I wrote about this in my latest Substack because these problems are fixable, but only if you stop looking at your dashboards and start examining your foundations.

If you’ve ever felt like your AI is “mostly fine” but your customers are telling a different story… you’ll relate to it.

Here’s how AI-Powered Agents can Automate the entire Competitive Intelligence process, from collecting signals to delivering insights:

𝟏. 𝐏𝐮𝐬𝐡 𝐔𝐩𝐝𝐚𝐭𝐞𝐬 𝐟𝐫𝐨𝐦 𝐒𝐨𝐮𝐫𝐜𝐞𝐬:
Monitor diverse sources like news, press, competitors, and social media for real-time updates. These updates are sent to an event bus (SNS, SQS, Kafka) or a webhook queue.

𝟐. 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐓𝐢𝐞𝐫𝐬:
Classify updates based on priority focusing on high-priority sources like pricing, launches, and funding. Medium-priority updates include blogs and case studies, while low-priority updates focus on reviews and trends.

𝟑. 𝐒𝐢𝐠𝐧𝐚𝐥 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐨𝐫 𝐀𝐠𝐞𝐧𝐭:
Aggregates, filters, deduplicates, and enriches signals by adding metadata, reducing noise by up to 90%.

𝟒. 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐀𝐠𝐞𝐧𝐭:
Retrieves competitor history and contextualizes each signal, categorizing it by urgency, impact, and relevance. This agent looks for patterns in competitor behavior.

𝟓. 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐬𝐭 𝐀𝐠𝐞𝐧𝐭:
Generates draft updates, suggests objection handlers, and creates win/loss matrices. It pulls insights from CRM data and produces content for reports or battle cards.

𝟔. 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐒𝐜𝐨𝐮𝐭 𝐀𝐠𝐞𝐧𝐭:
Monitors competitor activities, identifies opportunities, and surfaces vulnerabilities. It matches competitor movements with your sales pipeline to suggest talking points for sales teams.

𝟕. 𝐇𝐮𝐦𝐚𝐧-𝐢𝐧-𝐭𝐡𝐞-𝐋𝐨𝐨𝐩:
Provides oversight, ensuring AI-driven insights are validated and approved before use.

𝟖. 𝐌𝐨𝐝𝐞𝐥 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐋𝐚𝐲𝐞𝐫
AI models (like Amazon Bedrock, GPT, and Claude) analyze and enhance the intelligence gathered by agents.

𝟗. 𝐌𝐞𝐦𝐨𝐫𝐲 𝐚𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:
Store insights and historical data in systems like Redis, Upstash, and Amazon S3. Use analytics tools like Google Analytics and Mixpanel to measure usage and performance.

This is Agnetic AI at its best automating data collection, signal filtering, analysis, and decision-making processes for more efficient competitive tracking.

Is your organization ready to move from manual competitive analysis to intelligent automation?

Bias Assessment Tool

The shocking truth about how biased job postings are costing you top talent

43% of top candidates end up in the rejected folder due to bias

The Hidden Bias Crisis

Think your job postings are neutral? Think again. Your last job posting contained 14 bias indicators that are silently pushing away qualified candidates before they even apply.

73%

of diverse candidates skip biased job posts

2.3x

longer time-to-hire with biased language

$15K

average cost per mis-hire due to bias

Your Job Posting’s Hidden Bias Indicators

“Aggressive”Gender Biased

“Recent Graduate”Age Biased

“Culture-Fit”Diversity Eliminator

“Top College”Socioeconomic Bias

“Young & Dynamic”Age Discrimination

“Native Speaker”Language/Origin Bias

The Real Cost of Biased Hiring

When your job descriptions contain unconscious bias, you’re not just missing out on talent—you’re actively creating barriers that prevent the best candidates from even applying. Studies show that:

• Women are 32% less likely to apply to jobs with masculine-coded language
• Older workers skip 67% of age-biased postings
• Diverse candidates self-eliminate when they see “culture fit” requirements

🎯 Check Your Conscious Bias with ResumeGPTPro

Our AI-powered bias detection scans your job postings in real-time, identifying problematic language and suggesting inclusive alternatives.Scan Your Job Posting for FREE

The Path to Bias-Free Hiring

Equal hiring isn’t just about compliance—it’s about finding the best talent regardless of background. When you eliminate bias from your recruitment process, you:

• Access 2.3x larger talent pools
• Reduce time-to-hire by 40%
• Improve team performance by 35%
• Build stronger, more innovative teams

Take Action Today

Don’t let unconscious bias cost you another great hire. Start by auditing your current job postings and identifying language that might be turning away qualified candidates.

Remember: True diversity starts with inclusive language. Every word matters when you’re trying to build the best team possible.

#BiasFree #EqualHiring #Diversity #InclusiveRecruitment #ResumeGPTPro #TalentAcquisition

𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐂𝐚𝐥𝐥𝐢𝐧𝐠 = 𝐒𝐩𝐞𝐞𝐝 𝐃𝐢𝐚𝐥
LLM picks function
→ API responds
→ Done.

Perfect for: Known tasks, trusted environments, moving fast.

Note – LLM has direct access to your APIs. No bouncer at the door.

𝐌𝐂𝐏 = 𝐂𝐡𝐞𝐜𝐤𝐩𝐨𝐢𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦
Client evaluates
→ Routes through validation layer
→ Server picks tool
→ You control what happens.

Perfect for: Enterprise environments, but design with caution.

Note – It adds complexity.
And “safety” isn’t automatic – it’s just possible.

𝐌𝐂𝐏 𝐢𝐬𝐧’𝐭 𝐦𝐚𝐠𝐢𝐜𝐚𝐥𝐥𝐲 𝐬𝐚𝐟𝐞.
It’s a framework that gives you:
– Interception points (so you can validate requests)
– Server-side control (so you decide what’s exposed)
– Separation of concerns (so one bad call doesn’t nuke everything)

You still have to write the validation logic, define access controls, build the guardrails.

𝐖𝐡𝐞𝐧 𝐭𝐨 𝐮𝐬𝐞 𝐞𝐚𝐜𝐡?
Function Calling: Prototyping, internal tools, 1-2 predictable functions, you trust the LLM’s judgment.

MCP: Production systems, multiple tools, compliance requirements, you need audit trails, things break if the AI guesses wrong.

Function calling is fast and simple until you scale.
MCP is structured and controllable – but only if you actually build the controls.

Choose based on what happens when things go wrong, not when they go right.

#MCP #ToolCalling

Building AI Agents is not just about plugging in an LLM.
Scalable agents need an entire ecosystem of components working in sync.

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐜𝐨𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐥𝐨𝐜𝐤𝐬 𝐨𝐟 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬:

𝟏. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬
Frameworks like LangGraph, CrewAI, Autogen, and LlamaIndex allow developers to orchestrate multi-agent workflows, handle task decomposition, and structure agent communication.

𝟐. 𝐓𝐨𝐨𝐥 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧
Agents need to connect with APIs, databases, and code execution environments. Tool calling (OpenAI Functions, MCP) makes this possible in a structured way.

𝟑. 𝐌𝐞𝐦𝐨𝐫𝐲 𝐒𝐲𝐬𝐭𝐞𝐦
Without memory, agents become context-blind.

* Short-term: Manage session context.
* Long-term: Store facts in vector DBs like Pinecone or OpenSearch.
* Hybrid memory: Combine recall with reasoning for consistency.

𝟒. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐁𝐚𝐬𝐞
Vector databases and graph-based systems (Neo4j, Weaviate) form the backbone of knowledge retrieval, enabling semantic and hybrid search at scale.

𝟓. 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞
Handles task scheduling, retries, async operations, and scaling. This ensures the agent doesn’t just think, but also acts reliably and on time.

𝟔. 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞
Tools like Helicone and Langfuse track tokens, errors, and agent behavior. Governance ensures compliance, security, and responsible use.

𝟕. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭
Agents run across cloud, local, or edge setups using Docker or Kubernetes. CI/CD pipelines ensure continuous updates and scalable operations.

The future of AI agents is not just about smarter models.
It is about integrating frameworks, memory, tools, and governance to make them reliable, scalable, and production-ready.

𝐇𝐨𝐰 𝐦𝐚𝐧𝐲 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐥𝐚𝐲𝐞𝐫𝐬 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬?