DeFAI: How AI 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. While 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 multitude of chains, assets, and protocols.

Meanwhile, 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 serves as 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 are based on 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 Decentralized Finance, allowing users to input prompts for on-chain execution. They are often integrated with multiple chains and dApps, executing user intent while removing manual steps in complex transactions.

Some functions that these protocols can execute include:

• Swap, cross-chain, lending/withdrawing, cross-chain executing 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 a DEX - the abstract 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 Decentralized Finance strategies just like 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 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 to achieve 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 be trained on diverse, high-quality datasets to maintain effectiveness.

3. A comprehensive understanding of asset correlations, liquidity changes, and market sentiment is necessary to grasp 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 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 and provide trading recommendations to align with their preferences for long or short positions in certain assets.

Mode Synth Subnet

As the 50th subnet of a certain blockchain, Synth creates synthetic data for the financial prediction capabilities of agents. Compared to other traditional price prediction systems, Synth captures the full distribution of price movements and their associated probabilities, thus constructing the most accurate synthetic data in the world, supporting agents and LLM.

Providing more high-quality datasets can enable AI agents to make better directional decisions in trading, while predicting APY fluctuations under different market conditions, allowing liquidity pools to reallocate or withdraw liquidity when needed. Since the launch of the Autonomous Network, there has been strong demand from DeFi teams to integrate Synth data through their APIs.

Most Watched AI Agent Blockchain

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

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

These measures are implemented while they upgrade the network with AI, most notably by 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 prior to processing, ensuring on-chain security. As an L2 of a certain super chain, this blockchain stands in the middle ground, connecting human and agent users with the best Decentralized Finance ecosystem.

Comparison of Top Blockchains for AI Agents

Solana and Base are undoubtedly the two main chains for building and deploying most AI agent frameworks and tokens. AI agents leverage Solana's high throughput and low-latency network, along with the open-source ElizaOS, to deploy agent tokens, while Virtuals serves as the launchpad for deploying agents on Base. Although both have hackathons and funding incentives, in terms of their AI plans as a chain, they have not yet reached the level achieved by certain blockchains.

NEAR previously defined itself as an AI-centered L1 blockchain, with functionalities including an AI task marketplace, the NEAR AI Research Center with an open-source AI agent framework, and the NEAR AI Assistant. They recently announced a $20 million AI agent fund to expand fully autonomous and verifiable agents on NEAR.

Chainbase

Chainbase provides a fully verifiable on-chain structured dataset that can enhance the trading, insights, forecasting, and alpha discovery capabilities of AI agents. They launched manuscripts, a blockchain data streaming framework designed to integrate on-chain and off-chain datasets into a target data storage for unrestricted querying and analysis.

This enables developers to customize data processing workflows according to their specific needs. Standardizing and processing raw data into a clean and compatible format ensures that their datasets meet the stringent requirements of AI systems, thereby reducing preprocessing time while improving model accuracy and helping to create reliable AI agents.

Based on its extensive on-chain data, they have also developed a model called Theia, which translates on-chain data into user data analytics without any complex coding knowledge. The data utility of Chainbase is evident in their partnerships, where AI protocols are using their data to:

• ElizaOS Proxy Plugin, used for on-chain decision driving.
• Build Vana AI Assistant
• A certain social network intelligence that provides user behavior insights.
• Data analysis and prediction for Decentralized Finance
• Also collaborate with multiple projects

Compared to traditional data protocols, data protocols such as The Graph, Chainlink, and Alchemy provide data, but not in an AI-centric manner. The Graph offers a platform for querying and indexing blockchain data, providing developers with access to raw data that is not built for transactions or strategy execution. Chainlink provides oracle data feeds but lacks AI-optimized datasets for predictions, while Alchemy primarily offers RPC services.

In contrast, Chainbase data is specifically prepared blockchain data that can be easily utilized by AI applications or agents in a more structured and insightful manner, allowing agents to conveniently access data related to on-chain markets, liquidity, and token data.

sqd.ai

sqd.ai is developing an open database network tailored specifically for AI agents and Web3 services. Their decentralized data lake provides permissionless, cost-effective access to a vast amount of real-time and historical blockchain data, enabling AI agents to operate more efficiently.

sqd.ai provides real-time data indexing (including indexing of unconfirmed blocks), with indexing speeds of over 150,000+ blocks per second, faster than any other indexer. In the past 24 hours, it has delivered over 11TB of data, meeting the high throughput demands of billions of autonomous AI agents and developers.

Their customizable data processing platform provides tailored data based on the needs of AI agents, while DuckDB offers efficient data retrieval for local queries. Their comprehensive datasets support over 100 EVM and Substrate networks, including event logs and transaction details, which are invaluable for AI agents operating across multiple blockchains.

The addition of zero-knowledge proofs ensures that AI agents can access and process sensitive data without compromising privacy. Furthermore, sqd.ai can handle the increasing data load by adding more processing nodes, thus supporting a growing number of AI agents (estimated to reach billions).

Cookie

Cookie provides a modular data layer for AI agents and clusters, specifically designed for processing social data. It features an AI agent dashboard that tracks the top agent mindsets on-chain and on social platforms, and recently launched a plug-and-play data cluster API for other AI agents to detect popular narratives and mindset shifts in social media.

Their data pool covers over 7TB of real-time on-chain and social data sources, supported by 20 data agents, providing insights into market sentiment and on-chain analysis. Their latest AI agent utilizes 7% of their data pool's capacity to provide market predictions and discover new opportunities by leveraging various other agents operating beneath it.

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 transforms user intentions 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 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 about whale activity, liquidity changes, etc., while generating useful synthetic data for better predictive analytics and combining it with sentiment analysis from the general market, whether it is the token fluctuations of 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

Although AI agent tokens and frameworks have recently declined, 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 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 for agent decision-making.

Looking ahead, verifiability and