iAgent whitepaper
iAgent whitepaper
  • Introduction
  • Philosophy
    • Problem & Solution
    • Vision & Mission
  • Product and Ecosystem
    • Visual Learning Model (VLM)
    • ERC-AI Agent Standard
    • AI Agent Marketplace
    • Data Collection & Labeling
    • iAgent Dev Hub
  • Visual Learning Model Explained
  • iAgent Protocol Explained
  • Data Contribution Explained
  • Protocol Governance
    • Governance Framework
    • Governance Core
    • Privacy and Security
    • Terms & Conditions
  • Protocol Nodes
    • Node Deployment
    • Video Tutorial
    • FAQs
    • Key Aspects
    • Consensus Algorithms
    • Reward Calculation
    • How to KYC
    • Purchase and Sale Agreement
  • $AGNT Tokenomics
    • AGNT Contract
    • AGNT Vesting
    • AGNT Emission
    • AGNT Solo Staking
    • AGNT Liquid Staking
    • AGNT Governance
  • Roadmap
  • Conclusion
  • Official Links
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Product and Ecosystem

iAgent Introduces novel concepts such as 'VLM' to train AI agents from visual data, 'Proof of Work,’ and 'Proof of Ownership' for validating network tasks and verifies the quality of trained agents, while ERC standard managing the agents' ownership. It serves as the backbone of the system, promoting transparency, trust, and fairness within the protocol.

As part of our product offerings, we provide AI and game developers with all the essential tools, SDKs, APIs, and cost-effective computing power through DePIN framework, making high-quality AI training more accessible. Consequently, the iAgent protocol will provide grants and necessary support to other game devs so they can develop more game Agent modules efficiently and rapidly and therefore provide Agents for many more games from AAA to indie games simultaneously.

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Last updated 2 months ago

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