Abstraction

Maiga AI agent technology layers

Aggregators of multiple abstraction layer for AI + DeFi (DeFAI)

AI Agents (protocol):
- Description: MaigaAI are autonomous AI agents capable of performing tasks such as interacting with DeFi dapps, arbitraging crypto on CEX & DEX, or interacting with 3rd party on-chain applications.
- Example: Imagine a AI agent programmed to monitor top 10 meme prices. It can identify an new meme launch that matches specific criteria, evaluate its risk reward ratio and whales wallet interactions, and make a swap without requiring constant human decision
- Technical Insight: MaigaAI incorporates LLM and memory systems, enabling them to store and recall historical data points. These memories help refine decision-making tasks. For instance, an AI agent remembers previous trades and avoids repeating risky strategies that lost $.
Abstract AI (DeFAI):
- Description: MaigaAI provides "optimal paths," which gives the best optimal results comparing between several trades, effectively and efficiently. These paths enable the AI agents to navigate and interact with smart contracts, cross-chain swaps while preventing MEV sniping bots effectively.
- Example: An AI agent tasked with swapping one token for another can follow a optimal path to locate a decentralized exchange (DEX) and/or aggregator, perform the swap, and verify the transaction’s success.
- Technical Insight: These optimal paths are maintained in a on repo and memory module for the AI agent use for optimal path decision matrix process resulting in a most efficient and successful action.
User interaction layer (UX):
- Description: MaigaAI ’s user-friendly interface through Telegram bot command ensures that even non-technical users can deploy and manage AI agent using plain language commands.
- Example: A user can instruct an AI agent to "buy $100 of SOL when the price drops below $200," and the AI agent will autonomously execute the trade when conditions are met.
- Technical Insight: This is achieved using purpose-built language models (LLMs) tailored for blockchain operations, ensuring that commands are accurately interpreted and executed.
Community growth :
- Description: Developers can create or improve AI agent optimal paths and earn rewards for their contributions. Creators could create multiple profitable trading strategies to share with AI agent. 3rd party DeFAI AI founders could integrate their product solutions to be used with Maiga AI agents.
- Example: A developer submits a new feature for an AI agent to connect with an emerging DeFi protocol. If the integration is verified and used, the developer earns future tokens for every transaction that utilizes it.
- Technical Insight: Proposed optimal paths undergo verification by AI agents and their respective community reviewers to ensure accuracy and demand for the solution.
Proof of Trading PoT token model
- Description: MaigaAI incorporates the world’s 1st token model based on trading volume and liquidity.
- Example: $MAIGA serves as the base asset for AI agent tokens within the Maiga ecosystem. Each AI agent token is paired with $MAIGA in its respective liquidity pool, and creating a new AI agent requires a certain amount of $MAIGA tokens to unlock more functionalities.
- Technical Insight: All AI agent token is paired with the $MAIGA token in its respective liquidity pool. Creating a new AI agent requires MAIGA tokens, which are used to pair with the AI agent's liquidity pool. As these AI Agent LP pools are locked, this creates deflationary mechanics on $MAIGA tokens. As the demand for AI agent tokens grows, all transactions are routed through the MAIGA token. Users must swap their ETH (or other cryptocurrencies) into $MAIGA before swapping into any AI agent tokens. This flow generates buy pressure and trading volume for MAIGA token, similar to how SOL or BNB serve as the base currency in the Solana or Binance ecosystems, respectively.

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Last updated 2 months ago

Aggregators of multiple abstraction layer for AI + DeFi (DeFAI)

AI Agents (protocol):

Description: MaigaAI are autonomous AI agents capable of performing tasks such as interacting with DeFi dapps, arbitraging crypto on CEX & DEX, or interacting with 3rd party on-chain applications.
Example: Imagine a AI agent programmed to monitor top 10 meme prices. It can identify an new meme launch that matches specific criteria, evaluate its risk reward ratio and whales wallet interactions, and make a swap without requiring constant human decision
Technical Insight: MaigaAI incorporates LLM and memory systems, enabling them to store and recall historical data points. These memories help refine decision-making tasks. For instance, an AI agent remembers previous trades and avoids repeating risky strategies that lost $.

Abstract AI (DeFAI):

Description: MaigaAI provides "optimal paths," which gives the best optimal results comparing between several trades, effectively and efficiently. These paths enable the AI agents to navigate and interact with smart contracts, cross-chain swaps while preventing MEV sniping bots effectively.
Example: An AI agent tasked with swapping one token for another can follow a optimal path to locate a decentralized exchange (DEX) and/or aggregator, perform the swap, and verify the transaction’s success.
Technical Insight: These optimal paths are maintained in a on repo and memory module for the AI agent use for optimal path decision matrix process resulting in a most efficient and successful action.

User interaction layer (UX):

Description: MaigaAI ’s user-friendly interface through Telegram bot command ensures that even non-technical users can deploy and manage AI agent using plain language commands.
Example: A user can instruct an AI agent to "buy $100 of SOL when the price drops below $200," and the AI agent will autonomously execute the trade when conditions are met.
Technical Insight: This is achieved using purpose-built language models (LLMs) tailored for blockchain operations, ensuring that commands are accurately interpreted and executed.

Community growth :

Description: Developers can create or improve AI agent optimal paths and earn rewards for their contributions. Creators could create multiple profitable trading strategies to share with AI agent. 3rd party DeFAI AI founders could integrate their product solutions to be used with Maiga AI agents.
Example: A developer submits a new feature for an AI agent to connect with an emerging DeFi protocol. If the integration is verified and used, the developer earns future tokens for every transaction that utilizes it.
Technical Insight: Proposed optimal paths undergo verification by AI agents and their respective community reviewers to ensure accuracy and demand for the solution.

Proof of Trading PoT token model

Description: MaigaAI incorporates the world’s 1st token model based on trading volume and liquidity.
Example: $MAIGA serves as the base asset for AI agent tokens within the Maiga ecosystem. Each AI agent token is paired with $MAIGA in its respective liquidity pool, and creating a new AI agent requires a certain amount of $MAIGA tokens to unlock more functionalities.
Technical Insight: All AI agent token is paired with the $MAIGA token in its respective liquidity pool. Creating a new AI agent requires MAIGA tokens, which are used to pair with the AI agent's liquidity pool. As these AI Agent LP pools are locked, this creates deflationary mechanics on $MAIGA tokens. As the demand for AI agent tokens grows, all transactions are routed through the MAIGA token. Users must swap their ETH (or other cryptocurrencies) into $MAIGA before swapping into any AI agent tokens. This flow generates buy pressure and trading volume for MAIGA token, similar to how SOL or BNB serve as the base currency in the Solana or Binance ecosystems, respectively.