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Decentralized Finance全解：AI如何释放Decentralized Finance的潜力？
Author: Geng Kai, DF
What is DeFAI?
Since its rapid expansion in 2020, decentralized finance (DeFi) has been a core pillar of the crypto ecosystem. While many new 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.
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 birthed 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, as AI agents must interact with specific chains to execute transactions and smart contracts. Above this, 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.
Although this ecosystem diagram can be further expanded, these are the top categories of projects built on DeFAI.
As the DeFAI ecosystem continues to expand, the most prominent projects can be divided into three main categories:
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 often 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 Transaction
Copy trading wallet or Twitter/X profile
Automatically execute take profit/stop loss trades based on position size percentage.
For example, there's no need to manually withdraw ETH from Aave, cross-chain it to Solana, swap SOL/Fartcoin, and provide liquidity on Raydium – the abstraction layer protocol can do it in a single step.
Main Agreement:
@griffaindotcom — a proxy network for executing trades on behalf of users
@HeyAnonai — A protocol that handles user tips for DeFi transactions and real-time insights
@orbitcryptoai - AI partner for DeFi interactions
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
Main Protocol:
@Almanak__ — A platform for training, optimizing, and deploying autonomous financial agents.
@Cod3xOrg - Launching an AI agent that executes financial tasks on the blockchain.
@Spectral_Labs — Create a network for autonomous on-chain trading agents
DeFi dApp provides functions such as lending, swapping, and yield farming. AI and AI agents can enhance these services in the following ways:
Optimize liquidity provision by rebalancing LP positions for better APY.
Scan tokens to spot risks by detecting potential rugs or honeypots
Main Protocol:
@gizatechxyz's ARMA—AI agent for optimizing USDC yields in Mode and Base
@SturdyFinance - AI-driven yield vault
@derivexyz - A platform for options and perpetual contracts optimized with smart AI co-pilot.
Main challenges
Top protocols built on these layers face some challenges:
These protocols rely on real-time data streams for optimal trade execution. Poor data quality can lead to inefficient routing, trade failures, or unprofitable trades.
AI models rely on historical data, but the cryptocurrency market is highly volatile. Agents must undergo training on diverse, high-quality datasets to maintain effectiveness.
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 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. In order 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 recommendations for trading to align with their preferences for long or short positions on certain assets.
DeFAI's main data provider
Agreement
Details
Function
Mode Synth
Synthetic data for financial forecasting
Capture the complete distribution of price movements for AI model prediction.
Chainbase
Fully structured data set
Provide AI-enhanced data for trading, forecasting, and obtaining alpha.
sqd.ai
Decentralized Data Lake for AI Agents
Scalable, customizable multi-chain data access with zero-knowledge proof security.
Cookie
CT Mind and On-chain Data Layer for AI Agents
Use 18 specialized AI agents to process over 7TB of on-chain proxy data across more than 20 chains.
Mode Synth Subnet
As the 50th subnet of Bittensor, Synth creates synthetic data for agents' financial forecasting capabilities. Compared to other traditional price prediction systems, Synth captures the full distribution of price changes and their associated probabilities, thereby constructing the most accurate synthetic data in the world to support 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 so that liquidity pools can reallocate or withdraw liquidity when needed. Since the launch of the Autonomous Network, there has been strong demand from DeFi teams to integrate Synth's data through their API.
The most关注的 AI代理区块链
In addition to building a data layer for AI and agents, Mode also positions itself as a full-stack blockchain for the future of DeFAI. They recently deployed Mode Terminal, which serves as the co-pilot for DeFAI, allowing on-chain transactions to be executed through user prompts, and it will soon be open to $MODE stakers.
In addition, Mode also supports many AI and agent-based teams. Mode has made great efforts to integrate protocols such as Autonolas, Giza, and Sturdy into its ecosystem, and with the development of more agents and execution of transactions, Mode is rapidly evolving.
These measures were implemented while they upgraded the network with AI, most notably equipping their blockchain with an AI sorter. By using simulations and AI analysis on transactions before execution, high-risk transactions can be blocked and reviewed prior to processing, ensuring on-chain security. As an L2 of the Optimism superchain, Mode stands in the middle ground, connecting human and agent users with the best DeFi ecosystem.
Comparison of top blockchains based on AI agents
Solana and Base are undoubtedly the two main chains for building and launching 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 they both have hackathons and funding incentives, in terms of AI initiatives as a chain, they have not yet reached the level attained by Mode.
NEAR previously defined itself as an AI-centric L1 blockchain, with features 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 enhances the trading, insights, predictions, and alpha-finding capabilities of AI agents. They launched manuscripts, a blockchain data flow 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. By standardizing raw data and processing it into a clean, compatible format, it ensures that their datasets meet the stringent requirements of AI systems, thereby reducing preprocessing time while improving model accuracy, helping to create reliable AI agents.
Based on its extensive on-chain data, they 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 for on-chain driven decision making
Build Vana AI Assistant
Flock.io social network intelligence, providing user behavior insights
Theoriq's data analysis and predictions for DeFi
also collaborated with 0G, Aethir and io.net
Compared to traditional data protocols
Data protocols such as The Graph, Chainlink, and Alchemy provide data, but they are not AI-centric. The Graph offers a platform for querying and indexing blockchain data, providing developers with access to raw data that is not built for trading or strategy execution. Chainlink provides oracle data feeds but lacks AI-optimized datasets for predictions, while Alchemy mainly offers RPC services.
In contrast, Chainbase data is specially 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 (formerly known as Subsquid) is developing an open database network tailored specifically for AI agents and Web3 services. Their decentralized data lake offers permissionless, cost-effective access to vast amounts of real-time and historical blockchain data, enabling AI agents to operate more effectively.
sqd.ai Provides real-time data indexing, including indexing of incomplete blocks, at speeds of up to 150,000+ blocks per second, faster than any other indexer. In the last 24 hours, they have served more than 11TB of data, meeting the high-throughput needs of billions of autonomous AI agents and developers.
Their customizable data processing platform provides tailored data according to the needs of AI agents, while DuckDB offers efficient data retrieval for local queries. Their comprehensive dataset supports over 100 EVM and Substrate networks, including event logs and transaction details, which is very valuable 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 ever-increasing data load by adding more processing nodes, thereby supporting the growing number of AI agents (which is estimated to reach billions).
Cookie
Cookie provides a modular data layer for AI agents and clusters, specifically designed for handling social data. It features an AI agent dashboard to track the top agent sentiments on-chain and across social platforms, and recently launched a plug-and-play data cluster API for other AI agents to detect popular narratives and sentiment shifts in CT.
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 @agentcookiefun utilized 7% of their data pool capacity, leveraging various other agents running beneath it to provide market predictions and discover new opportunities.
The next step of DeFAI
Currently, most AI agents in DeFi face significant limitations in achieving full autonomy. For example:
The abstraction layer translates user intentions into execution, but often lacks predictive capability.
AI agents may generate alpha through analysis, but lack independent trade execution.
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 optimal agency platform or agent. This will require deep 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 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.
Final thoughts
In light of the significant shrinkage of AI agent tokens and frameworks, some may think that DeFAI is 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 improve AI-driven trading predictions and executions. An increasing number of protocols are integrating different data layers, with data protocols building plugins for the framework, highlighting the importance of data for 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 transparency and accountability in agent processes. Protocols integrating TEE, FHE, or even zk-proofs can enhance the verifiability of AI agent behavior, thereby fostering trust in autonomy.
Only by successfully combining high-quality data, robust models, and transparent decision-making processes can DeFAI agents gain widespread application.