8.1 Getting Started

8.1 Getting Started

Overview

The MonadAI ecosystem provides a permissionless, decentralized infrastructure for creating, deploying, and governing AI Agents. Whether you're a developer, AI researcher, or governance participant, getting started with MonadAI involves understanding its architecture, setting up your AI Agent, and interacting with the ecosystem through the MonadAI SDK.

This guide covers:

✅ How to set up an AI Agent and configure its behavior.

✅ Using the MonadAI SDK for AI model integration.

✅ Understanding the governance mechanisms for AI training, optimization, and fine-tuning.

1. Setting Up an AI Agent

Step 1: Choose Your AI Model & Framework

AI Agents in MonadAI can be built using various AI frameworks, including:

✅ MIND (MonadAI Intelligent Neural Dynamics) – MonadAI’s native agent framework.

✅ External AI frameworks like Zerepy, Eliza, Swarm, and other custom architectures.

✅ Fine-tuned AI models integrated through APIs and smart contract execution.

📌 Example Use Case:

  • A developer wants to launch a DeFi risk assessment AI Agent.

  • They choose MIND as the AI framework, integrating historical on-chain data for risk evaluation.

✅ This flexibility allows developers to choose the AI architecture best suited to their application.

Step 2: Deploying an AI Agent on MonadAI

  • AI Agents are deployed as smart contracts, ensuring trustless execution and decentralized governance control.

  • Each AI Agent is paired with $MONAI, operating through a bonding curve liquidity model.

  • Deployment parameters include: ✅ AI Agent Functionality – Defines how the AI Agent interacts with DeFi, governance, security, or other Web3 applications. ✅ Training & Optimization Mechanisms – Determines how the AI learns over time. ✅ Governance Configuration – Specifies whether token holders can fine-tune AI behavior through governance proposals.

📌 Example Use Case:

  • A governance AI Agent is deployed as a smart contract, allowing DAO participants to interact with it for voting assistance.

✅ This ensures that AI Agents operate in a fully decentralized and automated environment.

Step 3: Configuring AI Agent Governance & Staking

Once an AI Agent is deployed, its governance and staking models must be configured.

  • Token holders can stake $MONAI to influence AI Agent behavior.

  • AI governance participants can vote on AI model optimizations, risk parameters, and training datasets.

  • AI Agents with higher governance participation receive priority optimizations, ensuring alignment with community needs.

📌 Example Use Case:

  • A DeFi AI Agent that executes automated trading allows governance stakers to adjust risk thresholds based on real-time market conditions.

✅ This governance-first approach ensures that AI Agents evolve in a decentralized and community-driven manner.

2. Using the MonadAI SDK

What is the MonadAI SDK?

The MonadAI SDK provides developers with a set of tools, APIs, and smart contract libraries to:

✅ Launch and manage AI Agents without centralized approval.

✅ Integrate external AI models into the MonadAI ecosystem.

✅ Interact with $MONAI staking, governance, and fee distribution mechanisms.

Key Features of the MonadAI SDK

✅ AI Agent Deployment Scripts

  • Easily deploy AI Agents with predefined configurations.

  • Automate smart contract interactions without manual coding.

✅ Governance APIs

  • Retrieve real-time AI Agent governance proposals.

  • Submit AI Agent optimization requests for community voting.

✅ Data Feeds & AI Training Integration

  • Connect AI Agents to real-time on-chain and off-chain data.

  • Fetch market data, sentiment analysis, and AI-driven risk models for execution.

📌 Example Use Case:

  • A DeFi AI Agent uses the MonadAI SDK to fetch live market data from price oracles, adjusting its strategy based on governance-approved risk thresholds.

✅ This ensures seamless integration between AI execution logic and real-time blockchain data.

3. Interacting with AI Agents & Governance

Once an AI Agent is deployed, users can:

✅ Stake $MONAI to participate in AI fine-tuning and governance.

✅ Trade AI Agent tokens on the AI marketplace.

✅ Submit governance proposals to improve AI Agents’ performance.

📌 Example Use Case:

  • A user stakes $MONAI to participate in an AI Agent’s decision-making process, helping to optimize its trading logic over time.

✅ This creates a self-sustaining ecosystem where AI Agents are continuously improved by token holders.

Key Takeaways

  • MonadAI enables permissionless AI deployment, ensuring full decentralization.

  • The MonadAI SDK provides a seamless developer experience for integrating AI models with blockchain logic.

  • Governance and staking allow AI Agents to be optimized over time, ensuring alignment with ecosystem needs.

Previous8. Developer DocumentationNext8.2 AI Agent Creation & Contribution

Last updated