DeFAI：實現意圖的新途徑

DeFAI簡單來說就是AI+DeFi。市場對AI的炒作一遍又一遍，從AI算力到AI Meme

1. DeFAI tells the story of AI+DeFi

1.DeFAI講述AI+DeFi的故事

1.1 What is DeFAI

1.1 什麼是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 的簡明說法。市場對AI的炒作一遍又一遍，從AI算力到AI Meme，從不同的技術架構到不同的基礎設施。儘管近期AI Agent整體市值普遍下滑，但DeFAI概念正成為新的突破趨勢。目前的DeFAI大致可分為三類：AI抽象、自治式DeFi代理、市場分析預測。類別內的具體劃分如下圖所示。

1.2 How DeFAI works

1.2 DeFAI 的運作原理

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 系統中，AI Agent 的核心是 LLM（大型語言模型），涉及多層次的流程和技術，涵蓋了從資料收集到決策執行的各個方面。根據@3sigma在IOSG文章中的研究，大多數模型都遵循數據。採集、模型推理、決策、託管與營運、互通、錢包六個具體工作流程總結如下：

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. 資料收集：AI Agent 的首要任務是全面了解其運作環境。這包括從多個來源獲取即時數據：

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;

鏈上數據：透過索引器、預言機等取得交易記錄、智慧合約狀態、網路活動等即時區塊鏈數據，幫助代理商與市場動態保持同步；

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;

鏈下資料：從外部資料提供者（如CoinMarketCap、Coingecko）取得價格資訊、市場新聞和宏觀經濟指標，以確保代理商了解市場的外部狀況。這些資料通常透過API介面提供給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.

去中心化的資料來源：一些代理人可以透過去中心化的資料饋送協議來獲取價格預言機數據，以確保資料的去中心化和可信賴性。

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：資料收集完成後，AI Agent進入推理計算階段。在這裡，Agent 依靠多個 AI 模型進行複雜的推理和預測：

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;

監督和無監督學習：人工智慧模型可以透過對標記或未標記資料進行訓練來分析市場和治理論壇的行為。例如，他們可以透過分析歷史交易資料來預測未來市場趨勢，或透過分析治理論壇資料來預測未來市場趨勢。 ，推測投票提案的結果；

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;

強化學習：透過試誤和回饋機制，AI模型可以自主優化策略。例如，在代幣交易中，AI代理可以透過模擬多種交易策略來確定買入或賣出的最佳時機。這種學習方法使得Agent能夠在不斷變化的市場條件下不斷改進；

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.

自然語言處理（NLP）：透過理解和處理使用者自然語言輸入，智能體可以從治理提案或市場討論中提取關鍵訊息，幫助使用者做出更好的決策。這在掃描去中心化治理論壇或處理使用者指令時特別有用。何時尤為重要。

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.決策：根據收集到的數據和推理結果，AI Agent進入決策階段。在這個階段，Agent不僅需要分析目前的市場情勢，還需要在多個變數之間進行權衡：

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;

優化引擎：Agent使用優化引擎來尋找各種條件下的最佳執行計劃。例如，執行流動性提供或套利策略時，Agent必須考慮滑點、交易費用、網路延遲、資金規模等因素，以便找到最優的執行路徑；

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.

多智能體系統協作：為了因應複雜的市場狀況，單一智能體有時無法完全優化所有決策。在這種情況下，可以部署多個人工智慧代理，每個人工智慧代理專注於不同的任務領域，透過協作來改善整個系統的決策。效率。例如，一名代理商專注於市場分析，另一名代理商專注於執行交易策略。

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. 託管與運行：由於AI Agent需要處理大量運算，因此通常需要將其模型託管在鏈下伺服器或分散式運算網路上：

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;

集中託管：一些人工智慧代理可能依賴AWS等集中式雲端運算服務來託管其運算和儲存需求。這種方式有助於確保模型的高效運行，但也帶來了潛在的中心化風險；

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;

去中心化託管：為了降低中心化風險，一些代理商使用去中心化分散式運算網路（例如 Akash）和分散式儲存解決方案（例如 Arweave）來託管模型和資料。此類解決方案保證了模型的去中心化運行，同時提供資料儲存的持久性；

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.

鏈上交互：雖然模型本身託管在鏈下，但 AI Agent 需要與鏈上協議進行交互，以執行智能合約功能（例如交易執行、流動性管理）和管理資產。這需要安全的金鑰管理和交易。簽章機制，例如 MPC（多方運算）錢包或智慧合約錢包。

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

5. 互通性：AI Agent 在 DeFi 生態系中的關鍵角色是與