DeFAI: How AI Empowers Decentralized Finance?

Since its rapid development in 2020, decentralized finance ( DeFi ) has been a core pillar of the cryptocurrency ecosystem. Although many innovative protocols have been established, this has also led to increased complexity and fragmentation, making it difficult for even experienced users to navigate the numerous chains, assets, and protocols.

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

DeFAI spans multiple layers. The blockchain is the foundational layer, and AI agents must interact with specific chains to execute transactions and smart contracts. Above this, the data layer and computing layer provide the infrastructure needed to train AI models, which derive from 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. Finally, the agent framework allows 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 categorized into three main types:

1. Abstract Layer

Protocols built on this category serve as user-friendly interfaces similar to ChatGPT for Decentralized Finance, allowing users to input prompts for on-chain execution. They are typically integrated with multiple chains and dApps, executing user intentions while eliminating manual steps in complex transactions.

Some functions that these protocols can execute include:

• Swap, cross-chain, lend/withdraw, 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 Solana, swap for SOL/other tokens, and provide liquidity on the 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 and adjust their strategies based on new information. These agents can:

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

3. AI-Driven DApps

Decentralized Finance dApp offers lending, exchanging, and yield farming functions. AI and AI agents can enhance these services in the following ways:

• Optimize liquidity provision by rebalancing LP positions for better APY
• Scan tokens to identify risks by detecting potential rugs or honeypots.

Main Challenges

Top protocols built on these layers face some challenges:

1. These protocols rely on real-time data streams for optimal trade execution. Poor data quality can lead to route inefficiencies, trade failures, or unprofitable trades.

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

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

Protocols based on these categories have gained popularity in the market. However, in order to provide better products and optimal results, they should consider integrating various datasets of different qualities to elevate their products to a new level.

Data Layer - Powering DeFAI Smart

The quality of AI depends on the data it relies on. For AI agents to 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 further refine their trading strategies and reallocate resources.

High-quality datasets enable agents to better predict and analyze future price behavior, providing recommendations for trades to align with their preferences for long or short positions in certain assets.

The Most Attention-Grabbing AI Agent Blockchain

In addition to building a data layer for AI and agents, a certain blockchain also positions itself as a full-stack blockchain for the future of Decentralized Finance and AI. They recently deployed a terminal, which is the co-pilot for Decentralized Finance and AI, to execute on-chain transactions through user prompts, which will soon be open to token stakers.

In addition, the blockchain also supports many AI and agent-based teams. They have made tremendous efforts to integrate multiple protocols into its ecosystem, and with the development of more agents and execution of transactions, the blockchain is rapidly evolving.

These measures are implemented while they upgrade the network with AI, most notably equipping their blockchain with an AI sorter. By simulating and analyzing transactions with AI before execution, high-risk transactions can be stopped and reviewed before processing to ensure on-chain security. As an L2 scaling solution, the blockchain stands in the middle ground, connecting human and agent users with the best Decentralized Finance ecosystem.

The Next Step of DeFAI

Currently, most AI agents in Decentralized Finance face significant limitations in achieving full autonomy. For example:

1. The abstraction layer translates user intent into execution, but often lacks predictive capability.
2. AI agents may generate alpha through analysis, but lack independent trade execution.
3. AI-driven dApps can handle vaults or trades, but they are passive rather than active.

The next phase of DeFAI may focus on integrating useful data layers to develop the best proxy platform or agent. This will require deep on-chain data regarding whale activity, liquidity changes, etc., while generating useful synthetic data for better predictive analysis, along with sentiment analysis from the general market, whether it be token fluctuations in specific categories (such as AI agents, DeSci, etc.) or token fluctuations on social networks.

The ultimate goal is for AI agents to seamlessly generate and execute trading strategies from a single interface. As these systems mature, we may see future DeFi traders relying on AI agents to autonomously assess, predict, and execute financial strategies with minimal human intervention.

Conclusion

Given the significant shrinkage of AI agent tokens and frameworks, some may believe that DeFAI is just a flash in the pan. However, DeFAI is still in its early stages, and the potential for AI agents to enhance the usability and performance of Decentralized Finance is undeniable.

The key to unlocking this potential lies in obtaining high-quality real-time data, which will enhance AI-driven trading predictions and execution. An increasing number of protocols are integrating various data layers, and data protocols are building plugins for frameworks, highlighting the importance of data for agent decision-making.

Looking ahead, verifiability and privacy will become key challenges that protocols must address. Currently, most AI agents operate as a black box, and users must entrust their funds to them. Therefore, the development of verifiable AI decision-making will help ensure the transparency and accountability of agent processes. Protocols integrated with TEE, FHE, and even zk-proofs can enhance the verifiability of AI agent behavior, thereby achieving trust in autonomy.

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