8.2 AI Agent Creation & Contribution

8.2 AI Agent Creation & Contribution

Overview

AI Agents in MonadAI are modular, self-improving, and governed by decentralized participation. Developers and contributors can build AI Agents using MIND (MonadAI Intelligent Neural Dynamics) or integrate external AI frameworks like Zerepy, Eliza, Swarm, or custom AI models.

This section explains:

✅ How to build AI Agents using MonadAI’s modular core architecture.

✅ How developers can contribute AI training data, model optimizations, and governance improvements.

✅ How the ecosystem rewards AI contributions through governance and staking incentives.

By enabling permissionless AI development, MonadAI fosters continuous innovation, optimization, and refinement of autonomous AI entities.

1. Core Components of AI Agents in MonadAI

AI Agents in MonadAI are built with modular intelligence cores, allowing them to perform complex tasks, interact with smart contracts, and evolve over time.

The Three Foundational AI Cores in MIND

1️⃣ Cognitive Core – Manages logic, decision-making, and adaptability.

2️⃣ Voice Core – Handles AI communication, natural language processing, and user interactions.

3️⃣ Visual Core – Processes image recognition, visual analytics, and AI-generated content.

📌 Example Use Case:

• A DeFi AI Agent uses the Cognitive Core to analyze risk, detect arbitrage, and execute optimized trading strategies.

• A Virtual Influencer AI Agent uses the Voice Core to converse with users and generate engagement-driven responses.

✅ These modular cores enable AI Agents to be highly adaptable across DeFi, governance, gaming, and automation applications.

2. Developing & Deploying AI Agents

Step 1: Selecting an AI Framework

• Developers can choose to build AI Agents using: ✅ MIND (MonadAI’s native AI agent framework) for seamless blockchain integration. ✅ Zerepy, Eliza, Swarm, or custom architectures for specialized AI models. ✅ Pre-trained AI models integrated via SDKs and APIs.

📌 Example Use Case:

• A governance AI assistant is developed using MIND, allowing it to analyze DAO voting trends and propose governance insights.

✅ This ensures flexibility in AI development while maintaining on-chain governance and execution.

Step 2: Training & Fine-Tuning AI Agents

• AI Agents require continuous training to adapt to new market conditions, governance dynamics, or user interactions.

• Training data can be submitted by contributors, ensuring AI models learn from verified, governance-approved datasets.

📌 Example Use Case:

• A fraud detection AI Agent improves by integrating on-chain scam reports, security threat intelligence, and past fraud case studies.

✅ This allows AI Agents to evolve continuously without reliance on centralized oversight.

Step 3: Deploying the AI Agent to the MonadAI Ecosystem

• AI Agents are deployed as smart contracts, ensuring secure, trustless execution.

• Each AI Agent is paired with $MONAI, using the bonding curve liquidity model to determine price discovery.

• Governance participants can adjust AI parameters over time, fine-tuning execution logic based on real-world performance.

📌 Example Use Case:

• A risk-management AI Agent is deployed with governance-adjustable leverage settings, allowing the community to modify risk thresholds based on evolving market conditions.

✅ This ensures AI Agents remain adaptable and aligned with decentralized governance principles.

3. Contributing to AI Agent Development

Ways Developers & Researchers Can Contribute

✅ Submit AI Model Enhancements – Improve AI Agents by proposing better decision-making algorithms.

✅ Provide Training Datasets – Supply on-chain and off-chain data for model learning.

✅ Participate in AI Security Audits – Help identify and mitigate AI vulnerabilities.

✅ Optimize AI Execution Parameters – Adjust risk models, execution logic, and governance-driven optimizations.

📌 Example Use Case:

• A researcher submits a reinforcement learning algorithm that improves an AI Agent’s automated trading performance.

• The contribution is verified and rewarded with $MONAI, ensuring fair attribution and compensation.

✅ This model incentivizes high-quality contributions while ensuring AI Agents improve through decentralized collaboration.

4. Rewarding AI Contributions Through Staking & Governance

Contributor Rewards Model

• AI Agent contributions are recorded in the Decentralized Contribution Registry (DCR).

• Contributors receive rewards in $MONAI, with greater incentives for high-impact AI improvements.

• AI Agents can allocate a portion of their fee revenue to contributors, ensuring sustainable incentives.

📌 Example Use Case:

• A DeFi trading AI Agent generates profits from its execution fees.

• A percentage of its revenue is distributed to data providers and governance participants who helped train and optimize it.

✅ This ensures that AI Agents continuously evolve through decentralized economic incentives.

Key Takeaways

• AI Agents in MonadAI are modular, built with Cognitive, Voice, and Visual Cores.

• Developers can use MIND or external AI frameworks, ensuring flexible AI integration.

• Contributors can provide AI training data, optimizations, and security insights, improving AI Agent performance.

• AI contributions are rewarded through $MONAI, ensuring decentralized innovation.