# How it works?

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Maiga.ai redefines what it means to trade, earn, stake, and profit, in the era of Web3.0 and #DeFAI.
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## How Maiga AI agent works?

<figure><img src="/files/E5tSlNlxk4U0A006Zqr9" alt=""><figcaption></figcaption></figure>

## Creation, Paths, LLMs & MPC

#### **Maiga AI agent creation:**

* AI agents are created using Maiga’s native token, $MAIGA. This token acts as the foundation for activating and owning these agents.
  * **Example:** A user might spend $MAIGA to create an agent specialized in monitoring and trading DeFi assets. Once created, the AI agent is ready to receive tasks and training for personalized use cases.
  * **Technical:** The creation process initializes a connection between the AI agent and Maiga’s shared knowledge base, equipping with default libraries and capabilities.

#### **AI Agent Paths:**

* These are pre-built guides or roadmaps that help Maiga AI agents navigate blockchain networks efficiently.
  * **Example:** If an AI agent needs to perform a cross-chain token swap, it will use a specific path that outlines the necessary steps, including locating a bridge, ensuring liquidity, and completing the transaction.
  * **Technical:** These memories are maintained as part of a graph database, where nodes represent specific blockchain operations (e.g., smart contracts, DEXs). AI agents query the graph to identify optimal routes for task execution, reducing computational costs and errors.

#### **Reinforced Learnings:**

* AI agents can learn from past interactions, improving their decision-making and task efficiency over time.
  * **Example:** An AI agent trading agent might recognize patterns in market behavior and adjust its strategies to maximize returns based on previous successes and failures.
  * **Technical:** Memory systems categorize experiences into short-term (active tasks), long-term (cumulative knowledge), and pinned memories (key learnings). This architecture ensures agents adapt and refine their actions based on evolving conditions.
  * **Advanced Training:** Users can further enhance agents by providing domain-specific data, such as financial reports or specialized tutorials. This tailored training enables agents to specialize in tasks like market analysis, sentiment analysis or more.

#### **MPC Wallet Management:**

* AI agents come equipped with digital wallets, enabling them to securely store, trade, and manage blockchain assets.
  * **Example:** An AI agent might manage an EVM wallet, autonomously transferring funds to a staking protocol while ensuring compliance with user-defined conditions.
  * **Technical:** Maiga’s platform leverages Multi-Party Computation (MPC) wallet architecture. This ensures private keys are never fully exposed, adding an abstraction layer for enhanced security.
    * **Custom Wallets:** Users can import dedicated wallets to their agents for specific purposes. For instance, a wallet can be assigned to an AI agent managing DeFi strategies while isolating other funds.
    * **Abstraction Benefits:** By abstracting wallet management, agents can interact seamlessly across blockchains without compromising security. This abstraction also allows for smoother cross-chain operations, aligning with Maiga’s interoperability goals.

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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://maiga.gitbook.io/docs/maiga-tech/how-it-works.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.