# Problem & Solution

**Static Gaming Experience**\
Traditional gaming relies on static NPCs and bots to limit player interaction, making gameplay predictable and less engaging.

<details>

<summary>Solution Visual Learning Model (VLM)</summary>

iAgent introduces through VLM infra AI agents that mimic players' behavior, and offers AI agents that are smarter, adaptive, and more engaging in game environments.

</details>

**Underutilization of GPU Resources**\
Billions worth of idle GPU power owned by gamers, wasting valuable computational power.

<details>

<summary>Solution: Decentralized compute infra with Demand/Supply</summary>

iAgent’s decentralized infrastructure leverages these GPUs for AI training, rewarding contributors with AGNT tokens and creating Demand and Supply for GPU at the same time.

</details>

**Lack of Player Ownership and Monetization**\
With 3.32 billion gamers worldwide, most don’t see financial returns for their time and opportunities to monetize their skills/data are limited.

<details>

<summary>Solution: AI agent token standard (EIP)</summary>

iAgent turns gameplay into on-chain digital assets, giving players full ownership and multiple ways to monetize their skills while data/compute/security is intact. ERC-standard not limited to VLM.

</details>

**Data limitations for developers and rewards for gamers**\
Gamers aren't compelled to provide data to developers, and developers lack tools and data to generate gaming intelligence with AI.

<details>

<summary>Solution: <br>Open-source AI and Visual data monetization infra</summary>

iAgent’s open-source VLM infra and SDKs and API tools and labeled large visual data infra offers valuable insights to optimize AI agent deployments and contribution to the future of generative gaming intelligence as well as rewarding contributors same-time.

</details>


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