DeFAI: How AI Empowers Decentralized Finance?

Decentralized Finance ( DeFi ) has rapidly developed since 2020 and has become an important part of the crypto ecosystem. Although new innovative protocols emerge one after another, this has also led to increasing complexity and fragmentation of the system, making it difficult for even experienced users to cope with the numerous chains, assets, and protocols.

At the same time, artificial intelligence ( AI ) has shifted from the macro narrative of 2023 to a more specialized, agent-oriented focus in 2024. This transition has given rise to the emerging field of DeFi AI ( DeFAI ), where AI enhances the functionality of DeFi through automation, risk management, and capital optimization.

DeFAI covers multiple layers. The bottom layer is the blockchain, and AI agents must interact with specific chains to execute transactions and smart contracts. Above that are the data layer and the computing layer, which provide the infrastructure needed to train AI models based on historical price data, market sentiment, and on-chain analysis. The privacy and verifiability layer ensures that sensitive financial data remains secure while maintaining trustless execution. At the top layer is the agent framework, allowing developers to build specialized AI-driven applications, such as autonomous trading bots, credit risk assessors, and on-chain governance optimizers.

As the DeFAI ecosystem continues to expand, the most prominent projects can be divided into three main categories:

1. Abstract Layer

Such protocols act as user-friendly interfaces similar to ChatGPT for Decentralized Finance, allowing users to input prompts executed on-chain. They are often integrated with multiple chains and DApps, executing user intentions while simplifying manual steps in complex transactions.

The functions that these protocols can execute include:

• Exchange, cross-chain, lending, cross-chain execution transactions
• Copy trading wallet or social media profile
• Automatically execute take profit/stop loss trades based on position size percentage.

For example, there is no need to manually withdraw ETH from the lending platform, cross-chain it to other networks, swap tokens, and provide liquidity on DEX - the abstraction layer protocol can complete the operation in just one step.

2. Autonomous Trading Agent

Unlike traditional trading bots that follow preset rules, autonomous trading agents can learn and adapt to market conditions, adjusting their strategies based on new information. These agents can:

• Analyze data to continuously improve strategies
• Predict market trends to make better long and short decisions
• Execute complex Decentralized Finance strategies like basic trading.

3. AI-driven DApps

Decentralized Finance DApp provides functions such as lending, swapping, and yield farming. AI and AI agents can enhance these services in the following ways:

• Optimize liquidity supply by rebalancing LP positions for better APY.
• Scan tokens by detecting potential risks

Top protocols in these areas face some challenges:

1. Rely on real-time data streams for optimal trade execution. Poor data quality can lead to inefficient routes, trade failures, or unprofitable trades.

2. AI models rely on historical data, but the cryptocurrency market is highly volatile. Agents must accept training on diverse, high-quality datasets to maintain effectiveness.

3. It is necessary to have a comprehensive understanding of asset correlation, liquidity changes, and market sentiment in order to grasp the overall market conditions.

In order to provide better products and results, these protocols should consider integrating various high-quality datasets to enhance product standards.

Data Layer - Powering DeFAI Intelligence

The quality of AI depends on the data it relies on. To ensure that AI agents work effectively in DeFAI, they need real-time, structured, and verifiable data. For example, the abstraction layer needs to access on-chain data through RPC and social network APIs, while trading and yield optimization agents require data to refine trading strategies and reallocate resources.

High-quality datasets enable agents to better predict future price trends, providing trading suggestions to adapt to certain assets' long or short position preferences.

The main data providers of DeFAI include:

• Mode Synth: Synthetic data for financial forecasting, capturing the complete distribution of price changes, used for AI model predictions.
• Chainbase: A full-chain structured dataset that provides AI-enhanced data for trading, forecasting, and alpha generation.
• sqd.ai: A decentralized data lake for AI agents, providing scalable and customizable multi-chain data access with zero-knowledge proof security.
• Cookie: A social and on-chain data layer for AI agents, using multiple specialized AI agents to process on-chain agent data across multiple chains.

Mode Synth, as a subnet of Bittensor, creates synthetic data for the financial forecasting capabilities of agents. Compared to traditional price prediction systems, Synth captures the full distribution of price changes and their associated probabilities, constructing accurate synthetic data to support agents and LLMs.

Providing more high-quality datasets enables AI agents to make better directional decisions in trading while predicting APY fluctuations under different market conditions, allowing liquidity pools to reallocate or withdraw liquidity when necessary.

AI Agent Blockchain

In addition to building data layers for AI and agents, certain blockchains are positioning themselves as full-stack solutions for DeFAI. For example, Mode has deployed the DeFAI co-pilot to execute on-chain transactions based on user prompts. They also support many AI and agent-based teams, integrating multiple protocols into their ecosystem.

These measures are being implemented in parallel with their use of AI to upgrade the network, including equipping the blockchain with AI sorters. By using simulations and AI analysis of transactions before execution, high-risk transactions can be blocked and reviewed before processing, ensuring on-chain security.

Other mainstream blockchains such as Solana and Base are also important platforms for building AI agent frameworks and tokens. NEAR defines itself as an AI-centric L1 blockchain, offering functionalities like AI task markets and open-source AI agent frameworks.

The Future Development of DeFAI

Currently, most AI agents in Decentralized Finance still face limitations in achieving full autonomy. For example, the abstraction layer translates user intentions into execution but often lacks predictive capabilities; AI agents may generate alpha through analysis but lack independent trade execution; AI-driven DApps can handle vaults or trades, but are passive rather than proactive.

The next phase of DeFAI may focus on integrating useful data layers to develop the best agency platform. This will require deep on-chain data while generating useful synthetic data for better predictive analysis, along with market sentiment analysis.

The ultimate goal is for AI agents to seamlessly generate and execute trading strategies from a single interface. As the system matures, future DeFi traders may rely on AI agents to autonomously assess, predict, and execute financial strategies with minimal human intervention.

DeFAI is still in its early stages, and the potential of AI agents to enhance the availability and performance of Decentralized Finance cannot be ignored. Accessing high-quality real-time data is key to unlocking this potential, which will improve AI-driven trading predictions and executions.

In the future, verifiability and privacy will become key challenges that protocols must address. Integrating technologies based on TEE, FHE, and even zero-knowledge proofs can enhance the verifiability of AI agent behavior, thereby establishing trust in autonomy.

Only by successfully combining high-quality data, robust models, and transparent decision-making processes can DeFAI agents achieve widespread application.

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