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DeFAI: How AI Drives Innovation and Efficiency in Decentralized Finance
DeFAI: How AI Can Unlock the Potential of Decentralized Finance?
Decentralized Finance ( DeFi ) has been a core pillar of the crypto ecosystem since it rapidly expanded 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 )—a burgeoning 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. The data layer and computation 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
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:
For example, there is no need to manually withdraw ETH from the lending platform, cross-chain it to Solana, exchange it for SOL, and provide liquidity on the 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:
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:
Main Challenges
Top protocols built on these layers face some challenges:
These protocols rely on real-time data feeds to achieve optimal trade execution. Poor data quality may lead to inefficient routing, trade failures, or unprofitable trades.
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.
It is necessary to have a comprehensive understanding of asset correlation, liquidity changes, and market sentiment in order to understand the overall market situation.
Protocols based on these categories have been well-received 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 need 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 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 Decentralized Finance and AI. They recently deployed a terminal, which is the co-pilot for Decentralized Finance and AI, used to execute on-chain transactions through user prompts, and it will soon be open to token stakers.
In addition, the blockchain also supports many AI and agent-based teams. They have made tremendous efforts to integrate multiple protocols into its ecosystem, and as more agents are developed and execute transactions, the blockchain is rapidly evolving.
These measures are implemented while they upgrade the network with AI, the most notable being the addition of an AI sorter to their blockchain. By using simulation and AI analysis of transactions before execution, high-risk transactions can be blocked and reviewed prior to processing to ensure 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.
The Next Step of DeFAI
Currently, most AI agents in Decentralized Finance face significant limitations in achieving full autonomy. For example:
The abstraction layer transforms 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 stage of DeFAI may focus on integrating useful data layers to develop the optimal proxy platform or agent. This will require deep on-chain data regarding whale activity, liquidity changes, etc., while generating useful synthetic data for better predictive analysis, and combining it with sentiment analysis from the general market, whether it’s the volatility of tokens in specific categories like AI agents, DeSci, etc. (, 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 relying on AI agents to autonomously evaluate, predict, and execute financial strategies with minimal human intervention.
![DeFi Full Explanation: How AI Unlocks the Potential of DeFi?])https://img-cdn.gateio.im/webp-social/moments-1df1f707fb29db4dd351d64ceb0fd8b8.webp(
Final Thoughts
Given 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 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 the framework, highlighting the importance of data for 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. Integrating protocols based on 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 the DeFAI agents achieve widespread application.
![Full Analysis of DeFAI: How AI Unlocks the Potential of Decentralized Finance?])https://img-cdn.gateio.im/webp-social/moments-878bec495ad46b22ccff5200424900fe.webp(
![Decentralized Finance全解：AI如何释放Decentralized Finance的潜力？])https://img-cdn.gateio.im/webp-social/moments-56a89e79609d8f982d5d31dadfad9205.webp(