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

Decentralized Finance ( DeFi ) has been a core pillar of the crypto ecosystem since its rapid development in 2020. Although many innovative protocols have been established, it has also led to increased complexity and fragmentation, making it difficult for even experienced users to navigate through 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 that enhances Decentralized Finance through automation, risk management, and capital optimization.

DeFAI spans multiple layers. The blockchain serves as the foundational layer, and AI agents must interact with specific chains to execute transactions and smart contracts. The data layer and computation layer provide the infrastructure necessary to train AI models, which are derived 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 divided into three main categories:

1. Abstract Layer

Protocols built on this category serve as a user-friendly interface similar to ChatGPT for DeFi, 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:

• Exchange, cross-chain, lending/withdrawal, cross-chain execution of 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 lending platforms, transfer it across chains to other public chains, 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/short decisions.
• Execute complex DeFi strategies like basic trading.

3. AI-Driven DApps

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

• Optimize liquidity supply by rebalancing LP positions for better APY
• Scan tokens to detect potential rugs or honeypots to identify risks

Main Challenges

The 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 inefficient routing, transaction failures, or unprofitable trades.

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

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

Protocols based on these categories have gained popularity in the market. However, 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 Intelligence

The quality of AI depends on the data it relies on. To allow 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 future price behavior, providing recommendations for trading to align with their preferences for long or short positions on 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 DeFAI. They recently deployed an AI assistant, which serves as the co-pilot for DeFAI, to execute on-chain transactions through user prompts, and it will soon be open to token stakers of the chain.

In addition, the chain also supports many AI and agent-based teams. They have made great efforts to integrate multiple AI protocols into its ecosystem, and as more agents are developed and execute transactions, the chain is rapidly evolving.

These measures are implemented while they upgrade the network with AI, most notably equipping their blockchain with an AI sorter. By using simulation and AI analysis of transactions before execution, high-risk transactions can be blocked and reviewed before processing, ensuring on-chain security. As an L2 of a certain superchain, this chain stands in the middle ground, connecting human and agent users with the best DeFi ecosystem.

The Next Step of DeFAI

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

1. The abstraction layer converts 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 proxies. This will require in-depth on-chain data regarding whale activities, liquidity changes, etc., while generating useful synthetic data for better predictive analysis, and combining sentiment analysis from the general market, whether it concerns token fluctuations in specific categories like AI proxies, DeSci, etc. (, or token fluctuations on social networks.

The ultimate goal is for AI agents to be able 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 evaluate, predict, and execute financial strategies with minimal human intervention.

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Conclusion

In light of the significant shrinkage of AI agent tokens and frameworks, some may consider DeFAI to be just a flash in the pan. However, DeFAI is still in its early stages, and the potential of AI agents to enhance the usability and performance of DeFi 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 different data layers, and data protocols are building plugins for the framework, highlighting the importance of data in agent decision-making.

Looking to the future, verifiability and privacy will become key challenges that protocols must address. Currently, most AI agent operations remain a black box, and users must entrust their funds to it. Therefore, the development of verifiable AI decision-making will help ensure the transparency and accountability of agent processes. Protocols integrating TEE, FHE, and even zero-knowledge 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 achieve widespread application.

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