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

Decentralized Finance ( DeFi ) has rapidly developed since its emergence in 2020 and has been a core pillar of the crypto ecosystem. Despite the birth of many innovative protocols, 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 that enhances DeFi 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 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. 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

Such protocols act as user-friendly interfaces similar to ChatGPT for DeFi, allowing users to input prompts for on-chain execution. They often integrate with multiple chains and dApps, executing user intentions while eliminating manual steps in complex transactions.

Some of the functions that these protocols can execute include:

• Swap, cross-chain, lend/withdraw, cross-chain execute 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, bridge it to Solana, swap for SOL/other tokens, and provide liquidity on a DEX - the abstraction layer protocol can complete the operation in just one step.

2. Autonomous Trading Agent

Unlike traditional trading bots that follow predefined 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 refine strategies
• Predict market trends to make better long/short decisions
• Execute complex Decentralized Finance strategies like basic trading.

3. AI-driven DApps

Decentralized Finance dApp provides lending, swapping, yield farming and other 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 for risks by detecting potential rugs or honeypots.

The top protocols built on these layers face some challenges:

1. These protocols rely on real-time data streams to achieve 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 fully understand asset correlations, liquidity changes, and market sentiment in order to grasp the overall market situation.

To provide better products and optimal results, these protocols should consider integrating various datasets of different quality to elevate their products to a new level.

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 in certain assets.

The main data providers of DeFAI include:

• Mode Synth: Synthetic data used for financial forecasting, capturing the complete distribution of price fluctuations for AI model predictions.
• Chainbase: A fully structured dataset across the blockchain, providing AI-enhanced data for trading, prediction, and alpha acquisition.
• sqd.ai: A decentralized data lake for AI agents, featuring scalable and customizable multi-chain data access with zero-knowledge proof security.
• Cookie: A social media mindset and on-chain data layer oriented towards AI agents, using 18 specialized AI agents to process over 7TB of on-chain agent data across more than 20 chains.

The Most Watched AI Agent Blockchain

In addition to building a data layer for AI and agents, Mode also positions itself as a full-stack blockchain for the future of Decentralized Finance AI (DeFAI). They recently deployed Mode Terminal, which serves as the co-pilot for DeFAI, to execute on-chain transactions through user prompts.

Mode also supports many AI and agent-based teams that integrate multiple protocols into its ecosystem. With the development of more agents and execution of trades, Mode is rapidly evolving.

These measures are being implemented while they upgrade the network with AI, most notably equipping their blockchain with an AI sorter. By using simulations and AI analysis to analyze transactions before execution, high-risk transactions can be blocked and reviewed before processing to ensure on-chain security. As an L2 of the Optimism super chain, Mode stands in the middle ground, connecting human and agent users with the best 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 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 transactions, but they are passive rather than active.

The next stage 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 analytics and combining it with sentiment analysis from the general market.

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

Although AI agent tokens and frameworks have significantly shrunk, DeFAI is still in its early stages, and the potential of 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 executions. 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 ahead, verifiability and privacy will become key challenges that protocols must address. Currently, most AI agents operate as a black box, requiring users to 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, thus achieving trust in autonomy.

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