Can DeFAI, which deeply integrates DeFi and AI, give birth to a new wave of AI Agents?

Author: YBB Capital Researcher Ac-Core

1. What story does DeFAI tell?

1.1 What is DeFAI

DeFAI is AI+DeFi in a succinct way. 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, However, 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.

 Image source: self-made by the author

1.2 How DeFAI works

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:

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:

• 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;

• 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;

• 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.

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:

• 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;

• 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 can 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 useful in scanning decentralized governance forums or processing user instructions. It is especially important when.

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:

• 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;

• 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.

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:

• 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: 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;

• 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.

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

• Decentralized messaging: To ensure the synchronization of agents with on-chain protocols, agents can receive updates through decentralized messaging protocols such as IPFS or Webhooks. This allows AI agents to process external events in real time, such as voting on governance proposals. As a result, the liquidity pool changes, and the strategy is adjusted accordingly.

6. Wallet management: AI Agent 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 a single point of key risk. For example, Coinbase Replit’s wallet demonstrates how to use MPC for secure key management. This allows users to delegate some autonomous operations to AI Agents while maintaining a certain level of control;

• TEE (Trusted Execution Environment): Another common key management method is to use TEE technology to store private keys in a protected hardware enclave. This method enables AI Agents to conduct transactions in a completely autonomous environment. However, TEE currently faces problems with hardware centralization and performance overhead, but once these challenges are solved, fully autonomous AI systems will become possible.

1.3 The origin of the sect? From intention to DeFAI

 Image source: self-made by the author

If DeFAI’s vision is to enable users to manage their portfolios autonomously through AI agents and various AI platforms, making it easy for everyone to participate in crypto market transactions, does this vision naturally remind us of the concept of “intention”?

Let’s review the concept of “intent” first proposed by Paradigm. In normal transactions, we need to specify a clear execution path, such as exchanging Token A for Token B on Uniswap, but in the intent-driven scenario, the execution path is determined by the solver. 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 rearview mirror perspective, DeFAI's narrative is not only close to the AI Agent's The final concept perfectly follows the vision of achieving intent while being consistent with AI. Overall, DeFAI is more like a new path to intent.

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

2.1 Griffain

@griffaindotcom $GRIFFAIN: is an innovative platform that combines AI Agents with blockchain, which can help users to issue AI Agents, focusing on creating a powerful and scalable decentralized finance (DeFi) solution that supports seamless generation and Coin swaps, liquidity provision, and ecosystem growth. Easily manage wallets, transactions, and NFTs, and automate tasks such as Memecoin issuance and airdrops.

2.2 Hey Anon

@HeyAnonai $ANON: is an AI-driven DeFi protocol that simplifies interactions, aggregates real-time project data, performs complex operations through natural language processing, and facilitates the user's DeFi abstraction layer. DWF Labs announced support for the DeFAI project through its AI Agent Fund Hey Anon, and Moonshot will be launched on January 14th.

2.3 Orbit

@orbitcryptoai $GRIFT: Simplifies the complex DeFi interface and operations, lowering the threshold for ordinary people to participate.

Currently, it supports more than 100 blockchains and more than 200 protocols (EVM and Solana), and the token GRIFT is used to inject vitality into the platform.

2.4 Neur

@neur_sh $NEUR: is an open source full-stack application that brings together LLM models and blockchain technology capabilities, designed specifically for the Solana ecosystem, using the Solana Agent Kit for seamless protocol interactions.

2.5 Modenetwork

@modenetwork $MODE: It positions itself as the central platform for AI x DeFi innovation in Ethereum Layer2. Holders can stake MODE to obtain veMODE, thereby enjoying the airdrop of AI agents, and is committed to becoming the DeFAI Stack.

2.6 The Hive

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

2.7 Bankr

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

2.8 HotKeySwap

@HotKeySwap $HOTKEY: Provides a complete set of DeFi tools including AI-driven DEX aggregator 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. GEKKO token automated trading strategies include automatic rebalancing, yield harvesting, and creating new Token index functionality.

2.10 ASYM

@ASYM41b07 $ASYM: Provides AI-driven DEX aggregator and analytical tools to 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 the card game chain Parallel to help Agents navigate in the on-chain environment, execute transactions and interact with decentralized applications.

2.12 Slate

@slate_ceo $Slate: It is a general AI agent and agent connection infrastructure layer, which focuses on the execution of automated trading strategies, buying or selling under specific conditions, through natural language commands and translating them into on-chain operations. On-chain operations are as easy 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 Ensure its economic security.

2.15 HIERO

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

3. What system will the AI Agent end up in?

 Image source: self-made by the author

Every moment counts, and DeFAI projects are springing up like mushrooms after rain. After Bitcoin fell sharply to below $90,000 on January 13, the next day, according to Coingecko data, DeFAI-related tokens rose by 38.73%, including $GRIFT, $BUZZ and $ANON have the largest increase. However, it is worth considering how AI Agent should go in the financial direction. The current crossroads point to Game on the left and DeFi on the right.

3.1 Game to the left:

M3 (Metaverse Makers _) (@m3org) is perhaps 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 the 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, which is rich in manpower and resources, there has never been a truly popular AI game. The highly anticipated "Phantom Beast Palu" has sparked controversy over whether it uses AI design because of its development efficiency that is far beyond that of ordinary people, but the CEO eventually denied this statement. In addition, the long development cycle required by the game itself is much shorter than that of the game on the right. DeFI and AI Game seem to need more market enthusiasm.

3.2 DeFi to the right:

The project market capitalization ranks $GRIFFAIN, $ANON, $OLAS, $GRIFT, $SPEC, $BUZZ, $RSS3, $SNAI, and $GATSBY, among which the combined market capitalization of GRIFFAIN and ANON accounts for 37.29% of DeFAI's total market capitalization.

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 and fast transactions by generating wallets. It currently costs 0.01 Sol completes NFT minting 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 The founder of WAGMI has injected a lot of vitality into $ANON just by relying on the traffic. Hey Anon, as his next entrepreneurial project, currently ranks second with a market value of $248M.

IV. Summary

The emergence of DeFAI is not accidental. The core characteristics of blockchain are adapted to strong financial scenarios. Currently, both GameFAI and DeFAI have shown comparable market potential. The continuation of the inherited metaverse appears. With the help of AI, virtual property, roles, economy and other aspects can be managed. The elemental gameplay of AI Agent's reproduction Meme can be used to achieve the autonomy and prosperity of the autonomous evolution metaverse.

DeFi will inevitably move from passionate emotional speculation to an end point oriented towards actual value. The value of AI Agent cannot rely on issuing Memes to cater to market trends, but the continuation of the AI Agent story must have DeFi-like benefits. With the support of Matryoshka, the victorious king will not always be in armor, and the final outcome of the market competition is worth our anticipation.