Can DeFAI give birth to a new wave of AI agents?

Author: Ac-Core, YBB Capital Researcher

1. What story does DeFAI tell?

1.1 What is DeFAI?

To put it simply, DeFAI is AI+DeFi. The market has hyped up AI one round after another, from AI computing power to AI Meme, from different technical architectures to different infrastructures. Although the overall market value of AI Agents has generally fallen 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 in the categories are shown in the figure below.

Image source: self-made by the author

1.2 How does DeFAI work

In the DeFi system, AI Agent The core behind it is LLM (Large Language Model), which involves multi-level processes and technologies, covering everything from data collection to decision execution. According to the research of @3sigma in the IOSG article, most models follow six specific workflows: data collection, model reasoning, decision making, hosting and operation, interoperability, and wallets. The following is a summary: 1. Data collection: The first task of the AI ​​Agent is to have a comprehensive understanding of its operating environment. This includes obtaining real-time data from multiple sources: On-chain data: Obtain real-time blockchain data such as transaction records, smart contract status, and network activities through indexers, oracles, etc. This helps the Agent keep in sync with market dynamics;

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

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

• 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 enables Agents to continuously improve under changing market conditions;

• 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 important when scanning decentralized governance forums or processing user instructions.

3. Decision Making:Based on the collected data and the results of reasoning, AI Agents enter the decision-making stage. At this stage, the agent not only needs to analyze the current market conditions, but also make trade-offs between multiple variables:

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

• Multi-agent system collaboration: In order to cope with complex market conditions, a single agent is sometimes unable to fully optimize all decisions. In this case, multiple AI agents can be deployed, each focusing on different task areas, and collaborating to improve the decision-making efficiency of the overall system. For example, one agent focuses on market analysis, and another agent focuses on executing trading strategies.

4. Hosting and operation:Since AI Agents need to process a lot of calculations, their models usually need to be hosted on off-chain servers or distributed computing networks:

• 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;

• Decentralized hosting: In order 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 decentralized operation of the model while providing persistence of data storage;

• 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 transaction signing mechanisms, such as MPC (multi-party computing) wallets or smart contract wallets.

5. Interoperability:The key role of AI Agent in the DeFi ecosystem is to interact seamlessly with multiple different DeFi protocols and platforms:

• API integration: Agent exchanges data and interacts with various decentralized exchanges, liquidity pools, and lending protocols through API bridges. This allows the Agent to access key information such as market prices, counterparties, lending rates, etc. in real time, and make trading decisions accordingly;

• Decentralized messaging: To ensure the synchronization of the Agent with the on-chain protocol, the Agent can receive updates through a decentralized messaging protocol such as IPFS or Webhook. This allows the AI ​​Agent to process external events in real time, such as voting results of governance proposals and changes in liquidity pools, thereby adjusting strategies.

6. Wallet management:AI Agents must be able to perform actual operations on the blockchain, and all of this depends on its wallet and key management mechanism:

• MPC wallet: Multi-party computing wallets split private keys among multiple participants, allowing Agents to conduct transactions securely without single-point key risks. For example, Coinbase Replit's wallet demonstrates how to use MPC to achieve secure key management, which allows users to delegate AI Agents to perform partially autonomous operations while maintaining a certain degree of control;

• TEE (Trusted Execution Environment): Another common way to manage keys is to use TEE technology to store private keys in a protected hardware enclave. This method enables AI Agents to conduct transactions and make decisions in a completely autonomous environment without relying on third-party intervention. However, TEE currently faces problems with hardware centralization and performance overhead, but once these problems are solved, fully autonomous AI systems will become possible.

1.3 Origin of the sect? From Intent to DeFAI

Image source: self-made by the author

If the vision of DeFAI is: through AI agents and various AI Platform, enabling users to manage their portfolios independently, so that everyone can easily participate in crypto market transactions, then does this vision naturally remind us of the concept of "intention"?

Recalling the concept of "intention" first proposed by Paradigm. When we trade normally, we need to specify a clear execution path, just like exchanging Token A for Token B in Uniswap, but in the intent-driven scenario, the execution path is matched and finally determined by the solver and AI. In other words: transaction = I specify the execution method of TX; intention = I only want the TX result but not the execution process. From the perspective of the rearview mirror, DeFAI's narrative is not only close to the ultimate concept of AI Agent, but also perfectly catches up with the vision of realizing intention while fitting AI. Overall, DeFAI is more like a new added path of intention.

The ultimate version of realizing large-scale application of blockchain in the future will be: AI Agent + Solver + Intent - Centric + DeFAI = Future?

2. DeFAI related projects

Image source: self-made by the author

2.3 Orbit

@orbitcryptoai $GRIFT: It simplifies the complex DeFi interface and operations, and lowers the threshold for ordinary people to participate. It currently supports more than 100 blockchains and more than 200 protocols (EVM and Solana). The token GRIFT is used to inject vitality into the platform.

2.4 Neur

@neur_sh $NEUR: It is an open source full-stack application that brings together LLM models and blockchain technology functions. It is designed for the Solana ecosystem and uses the Solana Agent Kit to achieve seamless protocol interaction.

2.5 Modenetwork

@modenetwork $MODE: It is positioned as the central platform for AI x DeFi innovation on Ethereum Layer2. Holders can pledge MODE to obtain veMODE, thereby enjoying the airdrop of AI agents and striving to become the DeFAI Stack.

2.6 The Hive

@askthehive_ai $BUZZ: Built on Solana, it integrates multiple models including OpenAI, Anthropic, XAI, Gemini, etc. to realize complex DeFi operations such as transactions, pledges, and lending.

2.7 Bankr

@bankrbot $BNKR： It is an AI-driven cryptocurrency companion that allows users to easily buy, sell, exchange, place limit orders and manage wallets with just one message. It plans to add token exchange and on-chain tracking functions in the near future. The vision is to enable everyone to use DeFi and automate transactions.

2.8 HotKeySwap

@HotKeySwap $HOTKEY： It provides a complete set of DeFi tools such as AI-driven DEX aggregators and analysis tools, cross-chain transactions, and supports cross-chain transactions and analysis.

2.9 Gekko AI

@Gekko_Agent $GEKKO: An AI agent created by Virtuals Protocol, focusing on providing comprehensive automated trading solutions, an AI agent specifically made for prediction markets. Automated trading strategies for GEKKO tokens include automatic rebalancing, yield harvesting, and creating new token index features.

2.10 ASYM

@ASYM41b07 $ASYM: Provides AI-driven DEX aggregators and analytical tools that identify high ROI opportunities and settle the generated profits in $ASYM.

2.11 Wayfinder Foundation

@AIWayfinder $Wayfinder: An AI full-chain interactive tool launched by Parallel, a card game chain game, to help Agents navigate in the on-chain environment, perform transactions and interact with decentralized applications.

2.12 Slate

@slate_ceo $Slate: It is a general artificial intelligence agent and agent connection infrastructure layer, which uses natural language commands and translates them into on-chain operations, focusing on the execution of automated trading strategies, buying or selling under specific conditions, making on-chain operations as simple as thinking.

2.13 Cod3x

@Cod3xOrg $Cod3x: Solana AI hackathon project, providing code-free development tools to build agents that can automate DeFi strategies. Its Agentic Interface is a tool that can perform complex operations using only intent expressions.

2.14 Almanak

@Almanak__ $Almanak: An AI Agent with self-learning capabilities that can perform tasks autonomously, using agent-based modeling to optimize DeFi and gaming projects. Its mission is to use data science and trading knowledge to maximize the profitability of the protocol while ensuring its economic security.

2.15 HIERO

@HieroHQ $HTERM: A multi-chain intelligent tool for Solana and Base networks that allows users to use natural language command agents to autonomously complete transactions, including buying and selling tokens, performing simple token analysis, etc.

III. What kind of system does the AI ​​Agent belong to?

Image source: self-made by the author

3.1 Game to the left:

M3 (Metaverse Makers _) (@m3org) may be the most promising representative. The project is composed of artists and open source hacker communities suspected to be the organization behind ai16z. The core members of its team include JIN (@dankvr), Reneil (@reneil1337), Saori (@saori_xbt), Shaw (@shawmakesmagic), etc. However, the biggest practical obstacle for Game is that in the Web2 market with abundant manpower and resources, there has never been a truly popular AI game. The highly anticipated "Phantom Beast Palu" in January 2024 caused controversy about whether to adopt AI design because of its development efficiency far exceeding that of ordinary people, but the CEO eventually denied this statement. In addition, the long development cycle required by the game itself, compared with DeFI to the right, AI Game seems to need more market enthusiasm.

3.2 DeFi to the right:

The market value of the projects ranks $GRIFFAIN, $ANON, $OLAS, $GRIFT, $SPEC, $BUZZ, $RSS3, $SNAI, and $GATSBY, respectively. The combined market value of GRIFFAIN and ANON accounts for 37.29% of the total market value of DeFAI.

GRIFFAIN: Built on Solana, it currently ranks first in the DeFAI market value ranking with a market value advantage of $457M and 103,000 followers on Twitter. Its core function is to complete pointing transactions, fast transactions, and other functions by generating wallets. Currently, 0.01 Sol can be spent to complete the NFT casting of The Agent Engine.

Hey Anon: It adopts a multi-training mode and currently supports different public chains such as Sonic Insider, Solana, EVM, and opBNB. The sudden sprint of $ANON is completely driven by the aura of its founder Daniele (@danielesesta), who is also the founder of Wonderland, Abracadabra, and WAGMI. The traffic alone has injected a lot of vitality into $ANON. Hey Anon, as his next entrepreneurial project, currently ranks second with a market value of $248M.

Fourth, Summary

The emergence of DeFAI is not accidental. The core feature of blockchain is adapted to strong financial scenarios. At present, both GameFAI on the left and DeFAI on the right have shown comparable market potential. In the left direction of Game, the continuation of the inherited metaverse may appear in the future. With the help of AI, virtual property, roles, economy and other aspects can be managed. The elemental method of AI Agent's reproduction of Meme can be used to achieve the autonomy and prosperity of the self-evolving metaverse.

DeFi's development to the right will inevitably move from passionate emotional hype to an end point guided by actual value. The value of AI Agent cannot rely on the issuance of Meme to cater to market trends, but the continuation of the AI ​​Agent story must be supported by DeFi-like income nesting dolls. The victorious king will not always be in armor, and the final result of market competition is worth looking forward to.