DeFAI: How AI Can Unlock the Potential of Decentralized Finance?

Decentralized Finance (DeFi) has been at the core 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 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. The data layer and computation layer provide the infrastructure needed 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. 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

Such protocols serve as user-friendly interfaces similar to ChatGPT for DeFi, allowing users to input prompts executed on-chain. 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 a lending platform, bridge it to Solana, swap for SOL/other tokens, and provide liquidity on a DEX - the abstraction layer protocol can complete the operation in one step.

2. Autonomous Trading Agent

Unlike traditional trading robots 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 provides lending, exchanging, and yield farming functionalities. 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 identify risks by detecting potential rugs or honeypots.

The top protocols built on these layers face some challenges:

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

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

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

To provide better products and optimal results, these protocols should consider integrating various datasets of different quality.

Data Layer - Powering DeFAI Intelligence

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 future price behavior, providing trading recommendations to align with their preferences for long or short positions on certain assets.

The main data providers of DeFAI include:

1. Mode Synth: Synthetic data for financial forecasting, capturing the complete distribution of price movements for AI model predictions.

2. Chainbase: A full-chain structured dataset that provides AI-enhanced data for trading, forecasting, and acquiring alpha.

3. sqd.ai: A decentralized data lake for AI agents, offering scalable and customizable multi-chain data access with zero-knowledge proof security.

4. Cookie: A social media mindset for AI agents and an on-chain data layer, utilizing 18 specialized AI agents to process over 7TB of on-chain agent data across more than 20 chains.

Comparison of Top Blockchains on which AI Agents are Based

Some public chains are undoubtedly the main chains for building and deploying most AI agent frameworks and tokens. AI agents leverage the high throughput and low latency networks of these chains, as well as open-source operating systems, to deploy agent tokens. Although they all have hackathons and funding incentives, in terms of their AI plans as a chain, they have not yet reached the levels achieved by certain chains.

Some public chains previously defined themselves as AI-centered L1 blockchains, with functions including an AI task marketplace, an AI research center with an open-source AI agent framework, and AI assistants. They recently announced a large AI agent fund to scale fully autonomous and verifiable agents on-chain.

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 intentions into execution, but often lacks predictive capabilities.

2. AI agents may generate alpha through analysis, but lack independent trade execution.

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 proxy platform or agent. 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 it with sentiment analysis from the general market, whether it is the volatility of specific categories of tokens or the volatility of tokens 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 autonomously evaluating, predicting, and executing financial strategies with minimal human intervention, relying on AI agents.

Conclusion

In light of 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 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 for agent decision-making.

Looking ahead, verifiability and privacy will be key challenges that protocols must address. Currently, most AI agents operate as a black box, where 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 integrating TEE, FHE, or even zk-proofs can enhance the verifiability of AI agent behavior, thereby enabling 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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