DeFAI：实现意图的新途径

DeFAI简单来说就是AI+DeFi。市场对AI的炒作一遍又一遍，从AI算力到AI Meme

1. DeFAI tells the story of AI+DeFi

1.DeFAI讲述AI+DeFi的故事

1.1 What is DeFAI

1.1 什么是DeFAI

DeFAI is a concise way of saying AI+DeFi. The market has hyped AI over and over again, from AI computing power to AI Meme, from different technical architectures to different infrastructures. Although the overall market value of AI Agents has generally declined recently, the concept of DeFAI is becoming a new breakthrough trend. The current DeFAI can be roughly divided into three categories: AI abstraction, autonomous DeFi agent, and market analysis and prediction. The specific divisions within the categories are shown in the figure below.

DeFAI 是 AI+DeFi 的简明说法。市场对AI的炒作一遍又一遍，从AI算力到AI Meme，从不同的技术架构到不同的基础设施。尽管近期AI Agent整体市值普遍下滑，但DeFAI概念正在成为新的突破趋势。目前的DeFAI大致可分为三类：AI抽象、自治式DeFi代理、市场分析预测。类别内的具体划分如下图所示。

1.2 How DeFAI works

1.2 DeFAI 的运作原理

In the DeFi system, the core behind AI Agent is LLM (Large Language Model), which involves multi-level processes and technologies, covering all aspects from data collection to decision execution. According to the research of @3sigma in the IOSG article, most models follow the data. The six specific workflows of collection, model reasoning, decision making, hosting and operation, interoperability, and wallet are summarized below:

在 DeFi 系统中，AI Agent 的核心是 LLM（大型语言模型），涉及多层次的流程和技术，涵盖了从数据收集到决策执行的各个方面。根据@3sigma在IOSG文章中的研究，大多数模型都遵循数据。采集、模型推理、决策、托管与运营、互操作、钱包六个具体工作流程总结如下：

1. Data Collection: The first task for an AI Agent is to gain a comprehensive understanding of the environment in which it operates. This includes acquiring real-time data from multiple sources:

1. 数据收集：AI Agent 的首要任务是全面了解其运行环境。这包括从多个来源获取实时数据：

On-chain data: Obtain real-time blockchain data such as transaction records, smart contract status, and network activities through indexers, oracles, etc. This helps Agents keep in sync with market dynamics;

链上数据：通过索引器、预言机等获取交易记录、智能合约状态、网络活动等实时区块链数据，帮助代理与市场动态保持同步；

Off-chain data: Obtain price information, market news, and macroeconomic indicators from external data providers (such as CoinMarketCap, Coingecko) to ensure that the Agent understands the external conditions of the market. This data is usually provided to the Agent through an API interface;

链下数据：从外部数据提供商（如CoinMarketCap、Coingecko）获取价格信息、市场新闻和宏观经济指标，以确保代理了解市场的外部状况。这些数据通常通过API接口提供给Agent；

Decentralized data source: Some agents may obtain price oracle data through a decentralized data feed protocol to ensure the decentralization and credibility of the data.

去中心化的数据源：一些代理可以通过去中心化的数据馈送协议获取价格预言机数据，以保证数据的去中心化和可信性。

2. Model Reasoning: After data collection is completed, the AI Agent enters the reasoning and calculation phase. Here, the Agent relies on multiple AI models for complex reasoning and prediction:

2.模型推理：数据收集完成后，AI Agent进入推理计算阶段。在这里，Agent 依靠多个 AI 模型进行复杂的推理和预测：

Supervised and unsupervised learning: AI models can analyze the behavior of markets and governance forums by training on labeled or unlabeled data. For example, they can predict future market trends by analyzing historical trading data, or predict future market trends by analyzing governance forum data. , speculate the outcome of a voting proposal;

监督和无监督学习：人工智能模型可以通过对标记或未标记数据进行训练来分析市场和治理论坛的行为。例如，他们可以通过分析历史交易数据来预测未来市场趋势，或者通过分析治理论坛数据来预测未来市场趋势。 ，推测投票提案的结果；

Reinforcement learning: Through trial and error and feedback mechanisms, AI models can autonomously optimize strategies. For example, in token trading, AI Agents can determine the best time to buy or sell by simulating multiple trading strategies. This learning method allows Agent to continuously improve under changing market conditions;

强化学习：通过试错和反馈机制，AI模型可以自主优化策略。例如，在代币交易中，AI代理可以通过模拟多种交易策略来确定买入或卖出的最佳时机。这种学习方法使得Agent能够在不断变化的市场条件下不断改进；

Natural Language Processing (NLP): By understanding and processing user natural language input, Agents can extract key information from governance proposals or market discussions to help users make better decisions. This is especially useful in scanning decentralized governance forums or processing user instructions. It is especially important when.

自然语言处理（NLP）：通过理解和处理用户自然语言输入，智能体可以从治理提案或市场讨论中提取关键信息，帮助用户做出更好的决策。这在扫描去中心化治理论坛或处理用户指令时特别有用。何时尤为重要。

3. Decision making: Based on the collected data and the results of reasoning, the AI Agent enters the decision-making stage. In this stage, the Agent not only needs to analyze the current market situation, but also make trade-offs between multiple variables:

3.决策：根据收集到的数据和推理结果，AI Agent进入决策阶段。在这个阶段，Agent不仅需要分析当前的市场形势，还需要在多个变量之间进行权衡：

Optimization Engine: Agent uses the optimization engine to find the best execution plan under various conditions. For example, when performing liquidity provision or arbitrage strategies, Agent must consider factors such as slippage, transaction fees, network latency, fund size, etc. in order to find The optimal execution path;

优化引擎：Agent使用优化引擎来寻找各种条件下的最佳执行计划。例如，在执行流动性提供或套利策略时，Agent必须考虑滑点、交易费用、网络延迟、资金规模等因素，以便找到最优的执行路径；

Multi-agent system collaboration: In order to cope with complex market conditions, a single agent sometimes cannot fully optimize all decisions. In this case, multiple AI agents can be deployed, each focusing on different task areas, to improve the overall system's decision-making through collaboration. Efficiency. For example, one agent focuses on market analysis and another agent focuses on executing trading strategies.

多智能体系统协作：为了应对复杂的市场状况，单个智能体有时无法完全优化所有决策。在这种情况下，可以部署多个人工智能代理，每个人工智能代理专注于不同的任务领域，通过协作来改善整个系统的决策。效率。例如，一名代理专注于市场分析，另一名代理专注于执行交易策略。

4. Hosting and operation: Since AI Agent needs to process a lot of calculations, it is usually necessary to host its model on an off-chain server or distributed computing network:

4. 托管和运行：由于AI Agent需要处理大量计算，因此通常需要将其模型托管在链下服务器或分布式计算网络上：

Centralized hosting: Some AI agents may rely on centralized cloud computing services such as AWS to host their computing and storage needs. This approach helps ensure the efficient operation of the model, but it also brings potential risks of centralization;

集中托管：一些人工智能代理可能依赖AWS等集中式云计算服务来托管其计算和存储需求。这种方式有助于保证模型的高效运行，但也带来了潜在的中心化风险；

Decentralized hosting: To reduce the risk of centralization, some agents use decentralized distributed computing networks (such as Akash) and distributed storage solutions (such as Arweave) to host models and data. Such solutions ensure the model Decentralized operation while providing persistence of data storage;

去中心化托管：为了降低中心化风险，一些代理使用去中心化分布式计算网络（例如 Akash）和分布式存储解决方案（例如 Arweave）来托管模型和数据。此类解决方案保证了模型的去中心化运行，同时提供数据存储的持久性；

On-chain interaction: Although the model itself is hosted off-chain, the AI Agent needs to interact with the on-chain protocol in order to perform smart contract functions (such as transaction execution, liquidity management) and manage assets. This requires secure key management and transactions. Signing mechanisms such as MPC (Multi-Party Computation) wallets or smart contract wallets.

链上交互：虽然模型本身托管在链下，但 AI Agent 需要与链上协议进行交互，以执行智能合约功能（例如交易执行、流动性管理）和管理资产。这需要安全的密钥管理和交易。签名机制，例如 MPC（多方计算）钱包或智能合约钱包。

5. Interoperability: The key role of AI Agent in the DeFi ecosystem is to interact seamlessly with

5. 互操作性：AI Agent 在 DeFi 生态系统中的关键作用是与