{"id":17868,"date":"2025-03-18T23:42:30","date_gmt":"2025-03-18T23:42:30","guid":{"rendered":"https:\/\/dogewisperer.com\/?p=17868"},"modified":"2025-03-18T23:42:30","modified_gmt":"2025-03-18T23:42:30","slug":"iagent-secures-3m-launches-agnt-and-will-introduce-a-new-erc-standard-for-ai-agents","status":"publish","type":"post","link":"https:\/\/dogewisperer.com\/?p=17868","title":{"rendered":"iAgent Secures $3M, Launches $AGNT, and will Introduce a New ERC-** Standard for AI Agents"},"content":{"rendered":"
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[PRESS RELEASE \u2013 Vancouver, Canada, March 18th, 2025]<\/strong><\/p>\n

The artificial intelligence landscape is evolving. Traditional AI models remain centralized, relying on static text data and corporate control. iAgent <\/a>is introducing a decentralized AI framework where AI agents are trained, owned, and monetized on-chain using real-world visual data.<\/p>\n

Backed by a recent $3 million investment<\/strong>, iAgent has forged several strategic partnerships with leading projects such as Base, LayerZero, Avalanche, Aethir, Arbitrum, and XAI Games<\/strong>. With over 700,000 users<\/strong> on its AGNTxp platform, iAgent is at the forefront of AI x Gaming x DePIN innovation.<\/p>\n

Now, with TGE live, iAgent is laying the foundation for AI Agents to become a new digital asset that can be trained, validated, and exchanged transparently.<\/p>\n

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\u201cAI should be decentralized, transparent, and monetizable. iAgent is ensuring AI models belong to those who train them, not corporations.\u201d \u2014 Jamie Batzorig, Co-Founder of iAgent.<\/p>\n<\/blockquote>\n

iAgent\u2019s Product Hub: Powering the AI Economy<\/strong><\/p>\n

iAgent\u2019s ecosystem consists of a suite of interconnected products aimed at supporting AI development, deployment, and monetization through a decentralized framework. Each component is designed to enhance the verifiability, scalability, and accessibility of AI models across various ecosystems.<\/p>\n

Visual Learning Model (VLM): Adaptive AI Training<\/strong><\/p>\n

Unlike traditional AI that relies on text-based training (LLMs), iAgent\u2019s Visual Learning Models (VLMs) enable AI agents to learn from real-world gameplay footage. This approach allows AI to analyze, strategize, and adapt dynamically, making them more intelligent and applicable in real-world scenarios.<\/p>\n

iAgent\u2019s VLM-powered AI utilizes real-player interactions to enhance its adaptability, enabling continuous optimization for performance in competitive gaming environments and other applications:<\/p>\n

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