Original author: Geng Kai, DFG
What is DeFAI?
Decentralized finance (DeFi) has been a core pillar of the crypto ecosystem since its rapid expansion in 2020. While many new innovative protocols have been built, it has also led to increased complexity and fragmentation, with even experienced users struggling to navigate the vast number of chains, assets, and protocols.
Meanwhile, artificial intelligence (AI) has evolved from a broad-based narrative in 2023 to a more specialized, agent-oriented focus in 2024. This shift has given rise to DeFi AI (DeFAI) — an emerging field in which AI enhances DeFi through automation, risk management, and capital optimization.
DeFAI spans multiple layers. The blockchain is the foundational layer, as AI agents must interact with a specific chain to perform transactions and execute smart contracts. On top of this, the data layer and the compute layer provide the infrastructure needed to train AI models derived from historical price data, market sentiment, and on-chain analytics. The privacy and verifiability layers ensure that sensitive financial data remains secure while maintaining trustless execution. Finally, the agent framework allows developers to build specialized AI-driven applications such as autonomous trading bots, credit risk assessors, and on-chain governance optimizers.
While this ecosystem map could be expanded further, these are the top-level categories of projects built on DeFAI.
As the DeFAI ecosystem continues to expand, the most prominent projects can be divided into three main categories:
1. Abstraction layer
Protocols built on this category act as a user-friendly ChatGPT-like interface for DeFi, allowing users to input prompts for on-chain execution. They typically integrate with multiple chains and dApps and execute user intent while eliminating manual steps in complex transactions.
Some of the functions these protocols can perform include:
Exchange, cross-chain, lending/withdrawal, cross-chain transaction execution
Copy trading wallet or Twitter/X profile
Automatically execute take profit/stop loss and other transactions based on the position size percentage
For example, there is no need to manually withdraw ETH from Aave, cross-chain it to Solana, swap SOL/Fartcoin, and provide liquidity on Raydium - the abstraction layer protocol does it all in one step.
Main agreements:
@griffaindotcom — A network of brokers that execute trades for users
@HeyAnonai — A protocol for handling user alerts on DeFi transactions and real-time insights
@orbitcryptoai — An AI companion for DeFi interactions
https://x.com/griffaindotcom/status/1887682734027645055
2. Autonomous Trading Agent
Unlike traditional trading robots that follow preset rules, autonomous trading agents can learn and adapt to market conditions and adjust their strategies based on new information. These agents can:
Analyze data to continuously improve strategy
Predict market trends to make better long/short decisions
Execute complex DeFi strategies like basic trades
Main agreements:
@Almanak__ — A platform for training, optimizing, and deploying autonomous financial agents
@Cod3 xOrg — Launches AI agent that performs financial tasks on the blockchain
@Spectral_Labs — Creating a network of autonomous on-chain transaction agents
3. AI-driven DApps
DeFi dApps provide lending, exchange, yield farming, etc. AI and AI agents can enhance these services in the following ways:
Optimize liquidity provision by rebalancing LP positions to obtain better APY
Scan tokens for risks by detecting potential rugs or honeypots
Main agreements:
@gizatechxyz ’s ARMA — AI agent for optimizing USDC yields in Mode and Base
@SturdyFinance — AI-powered yield insurance
@derivexyz — Optimized options and perpetual contracts platform using smart AI co-pilot
Main Challenges
Top-level protocols built on these layers face several challenges:
These protocols rely on real-time data streams to achieve optimal trade execution. Poor data quality can lead to inefficient routing, failed trades, or unprofitable transactions.
AI models rely on historical data, but the cryptocurrency market is highly volatile. Agents must be trained on diverse, high-quality datasets to remain effective.
A comprehensive understanding of asset correlations, liquidity changes, and market sentiment is needed to understand the overall market situation
Protocols based on these categories have been well received by the market. However, in order to provide better products and optimal results, they should consider integrating various datasets of different qualities to take their products to the next level.
Data Layer - Powering DeFAI Intelligence
AI is only as good as the data it relies on. In order for AI agents to work effectively in DeFAI, they require real-time, structured, and verifiable data. For example, the abstraction layer needs to access on-chain data through RPC and social network APIs, while trading and yield optimization agents need data to further refine their trading strategies and reallocate resources.
High-quality datasets enable agents to perform better predictive analysis of future price behavior, providing trade recommendations to suit their preferences for long or short positions on certain assets.
DeFAIs main data provider
Mode Synth Subnet
As the 50th child network of Bittensor, Synth creates synthetic data for the financial prediction capabilities of agents. Compared to other traditional price prediction systems, Synth captures the full distribution of price changes and their associated probabilities, thereby building the worlds most accurate synthetic data to power agents and LLMs.
Providing more high-quality data sets can enable AI agents to make better directional decisions in trading, while predicting APY fluctuations under different market conditions so that liquidity pools can reallocate or extract liquidity when needed. Since the launch of the mainnet, they have had strong requests from DeFi teams to integrate Synths data through their API.
Most Watched AI Agent Blockchain
In addition to building a data layer for AI and agents, Mode is positioning itself to build a full-stack blockchain for the future of DeFAI. They recently deployed Mode Terminal, a co-pilot for DeFAI for executing on-chain transactions via user prompts, which will soon be available to $MODE stakers.
https://x.com/modenetwork/status/1882803123523383435?s=46t=JaMReQ6LUFL_qJEJqpfTPw
In addition, Mode also supports many AI- and agent-based teams. Mode has made great efforts to integrate protocols such as Autonolas, Giza, and Sturdy into its ecosystem, and Mode has grown rapidly as more agents are developed and execute transactions.
These initiatives were all achieved while they were upgrading the network with AI, most notably equipping their blockchain with an AI sorter. By using simulation and AI to analyze transactions before execution, high-risk transactions can be blocked and reviewed before processing to ensure on-chain security. As L2 of the Optimism superchain, Mode stands in the middle ground, connecting human and proxy users with the best DeFi ecosystem.
Comparison of top blockchains AI agents are based on
Solana and Base are undoubtedly the two main chains where most AI agent frameworks and tokens are built and released . AI Agents leverages Solana’s high throughput and low latency network and open source ElizaOS to deploy agent tokens, while Virtuals acts as a launchpad for deploying agents on Base. Although they both have hackathons and funding incentives, they have not yet reached the level that Mode has achieved in terms of their AI initiatives as a chain.
NEAR previously defined itself as an AI-centric L1 blockchain, with features including an AI task marketplace , the NEAR AI Research Center with an open source AI agent framework, and the NEAR AI Assistant . They recently announced a $20 million AI Agent Fund to scale fully autonomous and verifiable agents on NEAR.
Chainbase
Chainbase provides fully verifiable on-chain structured data sets that enhance AI agents’ trading, insights, forecasting, alpha finding, etc. They launched Manuscripts , a blockchain data flow framework for integrating on-chain and off-chain data into a target data store for unlimited querying and analysis.
This enables developers to tailor data processing workflows to their specific needs. Standardizing and processing raw data into a clean, compatible format ensures that their datasets meet the stringent requirements of AI systems, reducing preprocessing time while improving model accuracy and helping to create reliable AI agents.
Based on their extensive on-chain data, they have also developed a model called Theia , which translates on-chain data into data analytics for users without requiring any complex coding knowledge. Chainbase’s data utility is evident in their partnerships, where AI protocols are using their data to:
ElizaOS proxy plugin for on-chain driven decision making
Building the Vana AI Assistant
Flock.io social network intelligence, providing user behavior insights
Theoriqs data analysis and predictions for DeFi
Also working with 0G, Aethir and io.net
Compared with traditional data protocols
Data protocols such as The Graph, Chainlink, and Alchemy provide data but are not AI-centric. The Graph provides a platform for querying and indexing blockchain data, providing developers with raw data access that is not built for trading or policy execution. Chainlink provides oracle data feeds but lacks AI-optimized datasets for prediction, while Alchemy primarily provides RPC services.
In contrast, Chainbase data is specially prepared blockchain data that can be easily consumed by AI applications or agents in a more structured and insightful form, making it easier for agents to obtain data related to on-chain markets, liquidity, and token data.
sqd.ai (formerly Subsquid) is developing an open database network tailored for AI agents and Web3 services. Their decentralized data lake provides permissionless, cost-effective access to large amounts of real-time and historical blockchain data, enabling AI agents to operate more efficiently.
sqd.ai provides real-time data indexing (including indexing of outstanding blocks) at speeds of up to 150,000+ blocks per second, faster than any other indexer. In the past 24 hours, they have served over 11 TB of data , meeting the high-throughput needs of billions of autonomous AI agents and developers.
https://x.com/helloSQD/status/1879575591118414003
Their customizable data processing platform provides customized data based on the needs of AI agents, while DuckDB provides efficient data retrieval for local queries. Their comprehensive dataset supports more than 100 EVM and Substrate networks, including event logs and transaction details, which is very valuable for AI agents operating across multiple blockchains.
The addition of zero-knowledge proofs ensures that AI agents can access and process sensitive data without compromising privacy. In addition, sqd.ai can support the growing number of AI agents (estimated to be in the billions) by adding more processing nodes to handle the increasing data load.
Cookie
Cookie provides a modular data layer for AI agents and clusters, specifically designed to process social data. It features an AI agent dashboard that tracks top agent mindsets on-chain and on social platforms, and recently launched a plug-and-play data clustering API for other AI agents to detect popular narratives and mindset shifts in CT.
Their data cluster covers over 7 TB of real-time on-chain and social data feeds, powered by 20 data agents, providing insights into market sentiment and on-chain analytics. Their latest AI agent @agentcookiefun leverages their data cluster at 7% capacity, providing market forecasts and discovering new opportunities by leveraging various other agents running underneath it.
Next steps for DeFAI
Currently, most AI agents in DeFi face significant limitations in achieving full autonomy. For example:
Abstraction layers translate user intent into action, but often lack predictive capabilities
AI agents may generate alpha through analysis but lack independent trade execution
AI-driven dApps can handle vaults or transactions, but are reactive rather than proactive
The next phase of DeFAI will likely focus on integrating useful data layers to develop the best proxy platform or proxy. This will require deep on-chain data on whale activity, liquidity changes, etc., while producing useful synthetic data for better predictive analysis, combined with sentiment analysis from the general market, whether it is token fluctuations in specific categories (such as AI agents, DeSci, etc.) or token fluctuations on social networks.
The ultimate goal is for AI agents to be able to seamlessly generate and execute trading strategies from a single interface. As these systems mature, we may see a future where DeFi traders rely on AI agents to autonomously evaluate, predict, and execute financial strategies with minimal human intervention.
Final Thoughts
Given the significant shrinkage in AI agent tokens and frameworks, some may view DeFAI as a flash in the pan. However, DeFAI is still in its early stages, and the potential for AI agents to enhance DeFi usability and performance is undeniable.
The key to unlocking this potential is access to high-quality, real-time data, which will improve AI-driven trading predictions and execution. More and more protocols are integrating different data layers, with data protocols building plugins for frameworks, which highlights the importance of data for agent decision-making.
Going forward, verifiability and privacy will be key challenges that protocols must address. Currently, most AI agent operations remain a black box that users must trust with their funds. Therefore, the development of verifiable AI decisions will help ensure transparency and accountability of agent processes. Integrating TEE, FHE, or even zk-proofs-based protocols can enhance the verifiability of AI agent behavior, thereby enabling trust in autonomy.
Only by successfully combining high-quality data, robust models, and transparent decision-making processes can DeFAI agents gain widespread adoption.
About DFG
Digital Finance Group (DFG) is a global leading Web3 investment and venture capital firm founded in 2015. DFG manages over $1 billion in assets and invests in different areas of the blockchain ecosystem. Our portfolio has invested in more than 100 pioneering projects including Circle, Ledger, Coinlist, Near, Solana, Render Network, ZetaChain, etc.
At DFG, we are committed to creating value for our portfolio companies through market research, strategic consulting and sharing our vast resources globally. We are actively working with the most transformative and promising blockchain and Web 3.0 projects that are poised to revolutionize the industry.
DFG website: https://dfg.group
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