DeFAI: How Artificial Intelligence Can Unlock the Potential of Decentralized Finance?

Decentralized Finance ( DeFi ) has rapidly developed since 2020, becoming an important pillar of the crypto ecosystem. Although innovative protocols are emerging one after another, it has also led to an increasingly complex and decentralized 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 evolved from a popular narrative in 2023 to a more specialized, agent-oriented focus in 2024. This shift has given rise to the emerging field of DeFi AI ( DeFAI ), where AI enhances DeFi through automation, risk management, and capital optimization.

DeFAI covers multiple levels. The underlying layer is the blockchain, where AI agents need to interact with specific chains to execute transactions and smart contracts. Above that is the data layer and computation layer, which provide the necessary infrastructure for training AI models that utilize historical price data, market sentiment, and on-chain analysis. The privacy and verifiability layer ensures the security of sensitive financial data while maintaining trustless execution. At the top level 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 notable projects can be divided into three main categories:

1. Abstract Layer

Such protocols serve as user-friendly interfaces for Decentralized Finance, similar to ChatGPT, where users can perform on-chain operations by inputting prompts. They typically integrate multiple chains and dApps, simplifying manual steps in complex transactions while executing user intentions.

The executable functions of these protocols include:

• Swap, cross-chain, lending/withdrawal, cross-chain transaction execution
• Copy trading wallet or social media account
• Automatically execute take profit/stop loss and other trades based on position size percentage

For example, users do not need to manually withdraw ETH from the lending platform, transfer it across chains to other networks, exchange tokens, and provide liquidity on DEX - the abstract layer protocol can complete all operations in 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 optimize strategies
• Predict market trends and make more informed long and short decisions.
• Execute complex Decentralized Finance strategies, such as basic trading

3. AI-Driven DApps

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

• Optimize liquidity supply by rebalancing LP positions to achieve higher APY
• Scan tokens to discover potential risks, such as rug pulls or honeypots.

Top protocols in these areas face some challenges:

1. Real-time data streams are crucial for optimal trade execution. Poor data quality can lead to inefficient paths, trade failures, or unprofitable trades.

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

3. A comprehensive understanding of asset correlation, liquidity changes, and market sentiment is necessary to grasp the overall market conditions.

Protocols based on these categories have been recognized by the market. However, to provide higher quality products and results, they should consider integrating various high-quality datasets to elevate their products to new heights.

Data Layer - Powering DeFAI Intelligence

The performance of AI depends on the data it relies on. For AI agents to operate 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 optimize trading strategies and reallocate resources.

High-quality datasets enable agents to more accurately predict future price trends and provide trading recommendations to adapt to specific asset bullish or bearish preferences.

The Most Attention-Grabbing AI Agent Blockchains

In addition to building a data layer, a certain blockchain positions itself as a full-stack solution for the future of Decentralized Finance AI (DeFAI). They recently deployed a DeFAI collaborative tool for executing on-chain transactions through user prompts, which will soon be open to token stakers.

In addition, the blockchain also supports multiple AI and agent-based teams. As more agents develop and execute transactions, the ecosystem is rapidly evolving.

These measures are carried out in sync with the upgrade of network AI, the most notable of which is equipping the blockchain with an AI sorter. By using simulations and AI analysis of transactions before execution, high-risk transactions can be blocked before processing, ensuring on-chain security. As an L2 of a certain super chain, this blockchain is in an intermediate position, connecting human and agent users with a high-quality Decentralized Finance ecosystem.

The Next Step for DeFAI

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

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

The next phase of DeFAI may focus on integrating useful data layers to develop the best agent platform or agency. This requires deep on-chain data regarding whale activities, liquidity changes, etc., while generating useful synthetic data for more accurate predictive analysis, and combining it with overall market sentiment analysis, whether it is the token fluctuations of specific categories ( such as AI agents, DeSci, etc. ) or the token performance 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, future DeFi traders may rely on AI agents to autonomously assess, forecast, and execute financial strategies with minimal human intervention.

Conclusion

In light of the significant shrinkage of AI agent tokens and frameworks, some may view DeFAI as just a fleeting moment. However, DeFAI is still in its early stages, and the potential of AI agents to enhance the usability and performance of Decentralized Finance should not be overlooked.

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

Looking ahead, verifiability and privacy will become key challenges that protocols must address. Currently, most AI agents operate as black boxes, 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. Integrating protocols based on TEE, FHE, or even zero-knowledge proofs can enhance the verifiability of AI agent behavior, thereby building trust in autonomy.

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