Written by: 0xJeff, crypto Kol
Compiled by: zhouzhou, BlockBeats
Editor's note: In this article, Jeff discusses how DeFAI (Decentralized Finance + Artificial Intelligence) simplifies, optimizes, and enhances the DeFi experience through abstraction layers, automated trading agents, and AI-driven dApps, introducing several developing DeFAI projects such as Almanak, Cod3x, and Mode, and emphasizing the role of AI in enhancing trading strategies and managing portfolios.
The following is the original content (organised for readability):
DeFi has always been a pillar of Web3. It makes blockchain practical by providing tools to transfer funds instantly globally, invest in assets on-chain, lend without intermediaries, and stack strategies across DeFi protocols. This is the financial freedom that is within reach.
More importantly, DeFi addresses real-world problems. It allows unbanked individuals to access financial services, removes intermediaries, and operates around the clock, creating a truly global and inclusive financial system.
However, when we face reality and realize that DeFi is complex—setting up wallets, managing transaction fees, avoiding scams and rug pulls—it is not suited for the average person. The expanding L1, L2, and cross-chain ecosystems only complicate matters further. For most, the barriers to entry are simply too high.
This complexity has limited the growth of DeFi, but with the emergence of DeFAI, this situation is beginning to change.
What is DeFAI?
DeFAI (DeFi + AI) makes DeFi more accessible. By leveraging AI technology, it simplifies complex interfaces and removes barriers for ordinary people to participate. Imagine a world where managing your DeFi portfolio is as simple as chatting with ChatGPT.
The first wave of DeFAI projects has begun to emerge, primarily focused on three areas:
1. Abstraction Layer
2. Autonomous Trading Agents
3. AI-driven dApps
1. Abstraction Layer
The goal of abstraction layers is to hide the complexities of DeFi through intuitive interfaces. They allow users to interact with DeFi protocols through natural language commands without needing complex dashboards.
Before AI, abstraction layers like intent-based architectures simplified trade execution. Platforms like CoWSwap and symm io allow users to achieve the best pricing in decentralized liquidity pools, addressing liquidity fragmentation, but they do not solve the core issue: DeFi still feels complex.
Currently, AI-driven solutions are filling this gap:
Griffain is the first project to launch a token and is still in early access, requiring an invitation to use.
Griffain is more versatile, allowing users to perform a range of operations from simple to complex, such as task automation (DCA), publishing Memecoins, and conducting airdrops.
Orbit / Grift is the second project to launch a token, aimed at providing an on-chain DeFi experience. Orbit emphasizes cross-chain functionality, having integrated over 117 chains and 200 protocols, making it the most integrated among the three protocols.
Neur is the third project to launch a token, but due to its open-source nature, it quickly surpassed Orbit in valuation. Neur positions itself as the co-pilot for Solana, designed specifically for the Solana ecosystem. Neur is supported by Sendaifun's Solana Agent Kit.
I personally use slate ceo, which is still in its early stages and has not yet launched a token, but I love their automation features. I mainly use it to set conditional trades, like selling 25% of my position if xxxx reaches a $5 million market cap, or buying $5,000 worth of tokens if xxx reaches xxxx price.
AIWayfinder is another interesting project to watch. This is a behemoth built by the PRIME / ParallelTCG team, and it is worth looking forward to.
2. Autonomous Trading Agents
Why spend hours digging for Alpha, manually executing trades, and trying to optimize your portfolio when you can have an agent do it for you? Autonomous trading agents elevate trading bots to a new level, transforming them into dynamic partners that can adapt, learn, and make smarter decisions over time.
It is important to clarify that trading bots are not new. They have existed for years, executing predefined actions based on static programming. But agents are fundamentally different:
They extract information from unstructured and constantly changing environments.
They reason data in the context of their objectives.
They identify patterns and learn to leverage them over time.
They are capable of executing operations that their owners did not explicitly program.
This subfield is rapidly evolving; initially, agents may have only been for entertainment purposes—such as hyping some junk coins for fun—but have now shifted to more practical, profit-driven tools that help users trade more effectively. However, there remains an important challenge: how do you verify whether an 'agent' is merely a bot or if a person is operating behind the scenes?
This is where DeAI infrastructure plays a critical role.
The role of DeAI in verification agents
Key infrastructures like Trusted Execution Environments (TEE) ensure that agents can operate securely and without tampering.
For example:
TEE: Promoted by PhalaNetwork, TEE provides a secure isolation zone where data can be processed confidentially. Phala’s experiments—such as Unruggable ICO and Sporedotfun—demonstrate how agents can execute tasks while maintaining data integrity.
Transparent execution/verification frameworks: Innovations like zkML (zero-knowledge machine learning) or opML provide verifiability for reasoning and computation. Hyperbolic Labs' Proof-of-Sampling (PoSP) is a prominent example. This mechanism combines game theory and sampling techniques to ensure computations are accurate and efficient in a decentralized environment.
Why is this important?
As autonomous agents begin to handle significant TVL (assuming $100 million or more), users will demand assurances. They need to understand how agents manage risks, validate the frameworks they operate in, and ensure their funds are not randomly thrown at some junk coins.
This field is still in its early stages, but we have already seen some promising projects exploring these verifiability tools. As DeFAI evolves, this is a direction worth watching.
For more trends in DeAI infrastructure, please check out this article:
Top autonomous trading agents I am watching
Almanak
Almanak offers users institutional-grade quantitative AI agents that tackle complexity, fragmentation, and execution challenges in DeFi. The platform executes Monte Carlo simulations in real environments through a forked EVM chain, accounting for unique complexities such as MEV, transaction fees, and transaction ordering.
It utilizes TEE (Trusted Execution Environment) to ensure the privacy of strategy execution, protect Alpha information, and enables non-custodial fund management through Almanak Wallets, allowing precise permission delegation to agents.
Almanak's infrastructure supports the ideation, creation, evaluation, optimization, deployment, and monitoring of financial strategies. The ultimate goal is for these agents to learn and adapt over time.
Almanak raised $1 million on legiondotcc, with oversubscription. Next steps include releasing a beta version and deploying initial strategies/agents for testing among beta testers. It will be interesting to observe the performance of these quantitative agents.
Cod3xOrg / BigTonyXBT
Cod3x, created by the Byte Mason team (known for their work on Fantom and SonicLabs), is a DeFAI ecosystem designed to simplify the creation of trading agents. The platform provides a no-code building tool that allows users to create agents by specifying trading strategies, personalities, or even tweeting styles.
Users can access any dataset and develop financial strategies within minutes, utilizing a rich API and strategy library. Cod3x integrates with AlloraNetwork, using its advanced ML price prediction model to enhance trading strategies.
Big Tony is a flagship agent based on the Allora model, making predictions for entering and exiting mainstream cryptocurrencies. Cod3x is working to create a thriving ecosystem of autonomous trading agents.
A notable feature of Cod3x is its liquidity strategy. Unlike the common alt:alt LP structure promoted by virtuals io, Cod3x uses a stablecoin:alt LP supported by cdxUSD, its own CDP (Collateralized Debt Position). This structure provides more stability and confidence for liquidity providers compared to the volatility of alt:alt trading pairs.
Cod3x also has its own DeFi primitives, such as liquidity AMOs (Automated Market Operations) and mini pools, which enhance liquidity and add more functionality to agents within its ecosystem / DeFi Lego components.
Other notable projects
getaxal / Gekko Agent—Axal’s autonomous driving product where the agent handles complex multi-step crypto strategies. Gekko integrates autonomous driving capabilities. I’m waiting to see how Gekko performs data-driven trades in autonomous mode.
ASYM41b07—often referred to as the 'cheat code for Memecoin trading,' the ASYM agent can analyze vast amounts of data from blockchain and social media to predict Memecoin trends. ASYM has consistently outperformed the market and demonstrated 3-4x returns through backtesting. It will be interesting to see how it performs in live trading.
ProjectPlutus— I just love this name PPCOI
3. AI-driven decentralized applications (dApps)
AI-driven dApps are a promising but still nascent area within DeFAI. These are fully decentralized applications that integrate AI or AI agents to enhance functionality, automation, and user experience. While this area is still in its early stages, some ecosystems and projects are beginning to stand out.
In this field, modenetwork is a very active ecosystem, a Layer 2 network aimed at attracting high-tech AI x DeFi developers. Mode is the base for multiple teams working to develop cutting-edge AI-driven applications:
ARMA: A self-stablecoin mining solution tailored to user preferences, developed by gizatechxyz.
Modius: A self-agent supported by autonolas for mining Balancer LP.
Amplifi Lending Agents: Developed by Amplifi Fi, these agents are integrated with IroncladFinance, automatically swapping assets, lending on Ironclad, and maximizing returns through automatic rebalancing.
The core of this ecosystem is MODE, the native token. Holders can stake MODE to receive veMODE, gaining access to airdrops from AI agents, whitelist access to projects, and more ecosystem benefits. Mode is positioning itself as the center of AI x DeFi innovation, with its influence expected to grow significantly by 2025.
Additionally, danielesesta has garnered widespread attention with the DeFAI theory of HeyAnonai. He announced that HeyAnon is developing the following:
As an abstraction layer for DeFi interfaces
DeFi agents for autonomous trade execution
Research and communication agents for acquiring, filtering, and interpreting relevant data
The market has reacted enthusiastically, with the market cap of ANON tokens skyrocketing from $10 million to $130 million. Daniele seems to be bringing back the excitement of TIME Wonderland, but this time with a stronger foundation and a clearer vision.
In addition to these two ecosystems, many teams are building their own AI-driven decentralized applications. Once major ecosystems form around them, I will share more information in the future.
Final thoughts
DeFAI is transforming DeFi by making it smarter, simpler, and more accessible. With abstraction layers simplifying user interactions, autonomous trading agents managing portfolios, and AI-driven dApps optimizing use cases, we are witnessing the dawn of a new era.
Rather than saying the summer of DeFi was in 2020, it's more accurate to say that 2025 will be the summer of DeFAI.
