# Modular Architecture

### AI Agent Framework for Crypto Trading

AIFinflow provides powerful technical support for crypto investment through modular design. Developers can customize and develop AI agents based on the framework to suit different trading scenarios and DeFi applications. These agents are capable of:

* **Autonomously perceiving market changes** and automatically executing trades based on user-defined strategies.
* **Optimizing DeFi operations**, such as yield aggregation, liquidity management, and market arbitrage.
* **Making context-aware intelligent decisions** to help users gain a competitive advantage in complex market environments.

### Modular Architecture and Role File System

AIFinflow's modular architecture allows developers to flexibly add or modify functional modules as needed, providing limitless possibilities for diverse application scenarios:

**Modular Design**:\
Each functional unit is designed as an independent module. Developers can combine modules or create new ones to enhance the framework's flexibility and scalability.

**Role Configuration Files**:\
Each AI agent is equipped with a role configuration file that defines its behavior in DeFi investment, including risk preferences, investment strategies, communication style, and priorities. For example:

* **High-Risk Arbitrage Agent**: Focuses on arbitrage trading in high-volatility markets.
* **Yield Optimization Agent**: Prioritizes stable liquidity pools for yield maximization.

### Action System and Provider-Evaluator Mechanism

AIFinflow’s decision-making and execution process is divided into two main stages to ensure that each action of the AI agent is secure and reliable:

**Separation of Decision and Execution**:\
Strategy formulation and trade execution are handled separately. Strategies must undergo validation before execution, reducing trading risks.

**Context-Aware Feedback Mechanism**:\
The agent collects real-time market data (such as on-chain activity and price changes) through providers. Evaluators adjust strategies based on historical data and user feedback, continuously optimizing investment decisions.
