CorebroCore Whitepaper
  • Introduction
  • Getting Started
    • What is AI?
    • Vision & Mission
  • Basics
    • CerebroCore AI Architecture
    • Use Cases
    • AI Agents
    • $CCAI Token Utility
    • Tokenomics
    • Security & Transparency
    • Governance & Community
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  • 🧱 1. Modular Agent Layer
  • 🔐 2. Access Control Layer
  • ⚙️ 3. Execution Layer (API Gateway)
  • 🧬 4. Customization & Personalization
  • 🌐 5. Integration & Developer SDKs
  • 🗳️ 6. Governance Layer (DAO)
  • 🧩 7. Data Privacy & Security
  1. Basics

CerebroCore AI Architecture

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Last updated 5 days ago

CerebroCore AI is built upon a layered, modular architecture designed to support scalable, secure, and decentralized interaction with intelligent agents. The system is designed to separate concerns between AI logic, access control, user governance, and integration.

🧱 1. Modular Agent Layer

At the core of CerebroCore is a network of modular AI agents, each trained or optimized for a specific function (e.g. coding, art generation, tutoring). These agents are:

  • Stateless, task-specific, and interchangeable

  • Prompt-driven, compatible with LLM backends (like GPT, Claude, or open-source models)

  • Customizable, via user preferences or NFT Access Passes

  • Sandboxed, ensuring safety and context isolation

Each agent lives within a secure API environment and can evolve through reinforcement learning or community contribution.

🔐 2. Access Control Layer

CerebroCore uses a token-gated access mechanism to interact with agents and platform features. Key components:

  • $CCAI token for access rights

  • Wallet login (e.g., MetaMask, WalletConnect)

  • NFT Access Passes to unlock personalized agents or premium usage tiers

  • Usage credits for interaction frequency, customizable per agent

Access to APIs, dashboards, or external integrations is always on-chain verified, reducing fraud and misuse.

⚙️ 3. Execution Layer (API Gateway)

All agent interactions are routed through a secure Execution Layer which includes:

  • Rate limiting & metering based on token ownership

  • Interaction logging (optionally anonymized)

  • Webhooks & WebSocket endpoints for live integrations

  • Multi-model backend routing (choose which LLM to use per task)

This layer acts as the interface between users, developers, and the agent framework.

🧬 4. Customization & Personalization

Users can personalize their AI agents through:

  • NFT-bound traits (e.g., tone, expertise, behavior presets)

  • Custom datasets or prompt memory (stored off-chain or encrypted IPFS)

  • Interface preferences per user (text/voice)

This enables users to build persistent, agent-like AI personalities with unique characteristics tied to their wallet or NFT identity.

🌐 5. Integration & Developer SDKs

CerebroCore is designed to be extensible via:

  • Public APIs for frontend apps, bots, or tools

  • Agent SDK for building and hosting your own AI agent

  • Webhook triggers to connect AI output to 3rd-party workflows (e.g., Discord, Slack, Zapier)

We plan to support integrations with platforms such as Notion, Figma, GitHub, and Telegram for seamless workflow automation.

🗳️ 6. Governance Layer (DAO)

Decisions around platform updates, agent evolution, and ecosystem funding are managed via on-chain governance. This includes:

  • Proposal system (submit + vote using staked $CCAI)

  • Treasury management for grants, audits, or bounties

  • Community-curated agent marketplace

  • Open roadmap voting (prioritize which agents to release next)

Staking and participation incentives ensure governance remains active and inclusive.

🧩 7. Data Privacy & Security

We prioritize user safety and transparency:

  • No PII storage by default

  • Encrypted prompts if stored temporarily

  • Open-source smart contracts on Ethereum

  • Audited AI model APIs

  • Verifiable agent outputs (via prompt signing)

Optional ZK-integrations are being researched for future privacy guarantees on agent interactions.