Original Author: Geng Kai, DFG
What is DeFAI?
Since its rapid expansion in 2020, decentralized finance (DeFi) has been a core pillar of the crypto ecosystem. While many new innovative protocols have been established, it has also led to increased complexity and fragmentation, making it difficult for even experienced users to navigate the multitude of chains, assets, and protocols.
At the same time, artificial intelligence (AI) has evolved from a broad foundational narrative in 2023 to a more specialized, agent-oriented focus in 2024. This shift has given rise to DeFi AI (DeFAI) — an emerging field where AI enhances DeFi through automation, risk management, and capital optimization.
DeFAI spans multiple layers. The blockchain serves as the foundational layer, as AI agents must interact with specific chains to execute transactions and smart contracts. Above this, the data layer and computation layer provide the infrastructure needed to train AI models, which are based on historical price data, market sentiment, and on-chain analysis. The privacy and verifiability layer ensures 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 can be further expanded, these are the top categories of projects built on DeFAI.
As the DeFAI ecosystem continues to grow, the most prominent projects can be categorized into three main categories:
1. Abstraction Layer
Protocols built on this category act as user-friendly interfaces similar to ChatGPT for DeFi, allowing users to input prompts for on-chain execution. They are often integrated with multiple chains and dApps, executing user intentions while eliminating manual steps in complex transactions.
Some functions these protocols can perform include:
Swapping, cross-chain, lending/withdrawing, executing cross-chain transactions
Following trading wallets or Twitter/X profiles
Automatically executing trades like take profit/stop loss based on position size percentage
For example, instead of manually withdrawing ETH from Aave, cross-chain to Solana, swapping SOL/Fartcoin, and providing liquidity on Raydium — the abstraction layer protocol can complete the operation in one step.
Key Protocols:
@griffaindotcom — A network of agents that execute trades for users
@HeyAnonai — A protocol that handles user prompts for DeFi trading and real-time insights
@orbitcryptoai — An AI partner for DeFi interactions
https://x.com/griffaindotcom/status/1887682734027645055
2. Autonomous Trading Agents
Unlike traditional trading bots 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 refine strategies
Predict market trends to make better long/short decisions
Execute complex DeFi strategies like basic trading
Key Protocols:
@Almanak__ — A platform for training, optimizing, and deploying autonomous financial agents
@Cod3xOrg — Launching AI agents that execute financial tasks on the blockchain
@Spectral_Labs — A network for creating autonomous on-chain trading agents
3. AI-Driven DApps
DeFi dApps provide functionalities such as lending, swapping, and yield farming. AI and AI agents can enhance these services by:
Optimizing liquidity supply for better APY through rebalancing LP positions
Scanning tokens for risks by detecting potential rugs or honeypots
Key Protocols:
@gizatechxyz ARMA — An AI agent for optimizing USDC yields in Mode and Base
@SturdyFinance — An AI-driven yield vault
@derivexyz — An options and perpetual contract platform optimized with smart AI co-pilot
Major Challenges
Top protocols built on these layers face several challenges:
These protocols rely on real-time data streams for optimal trade execution. Poor data quality can lead to inefficient routing, failed trades, or unprofitable transactions
AI models depend 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 grasp the overall market conditions
Protocols based on these categories have gained popularity in the market. However, to provide better products and optimal outcomes, they should consider integrating various datasets of different qualities to elevate their offerings to a new level.
Data Layer — Powering DeFAI Intelligence
The effectiveness of AI depends on the data it relies on. For AI agents to work effectively in DeFAI, they need real-time, structured, and verifiable data. For instance, the abstraction layer requires access to 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 better predict future price behavior, providing trading suggestions that align with their preferences for long or short positions on certain assets.
Major Data Providers for DeFAI
Mode Synth Subnet
As the 50th subnet of Bittensor, Synth creates synthetic data for agents' financial forecasting capabilities. Compared to other traditional price prediction systems, Synth captures the complete distribution of price changes and their associated probabilities, thus building the world's most accurate synthetic data to support agents and LLMs.
Providing more high-quality datasets can enable AI agents to make better directional decisions in trading while predicting APY fluctuations under different market conditions, allowing liquidity pools to reallocate or withdraw liquidity when needed. Since the launch of the autonomous network, they have seen strong demand from DeFi teams to integrate Synth's data through their API.
Most Notable AI Agent Blockchains
In addition to building a data layer for AI and agents, Mode positions itself as a full-stack blockchain for the future of DeFAI. They recently deployed Mode Terminal, which serves as a co-pilot for DeFAI, executing on-chain trades through user prompts, and will soon be open to $MODE stakers.
https://x.com/modenetwork/status/1882803123523383435?s=46&t=JaMReQ6LUFL_qJEJqpfTPw
In addition, Mode supports many AI and agent-based teams. Mode has made significant efforts to integrate protocols such as Autonolas, Giza, and Sturdy into its ecosystem, rapidly developing as more agents are created and transactions are executed.
These initiatives are being implemented while they upgrade the network with AI, most notably by equipping their blockchain with an AI sorter. By using simulations and AI analysis before execution, high-risk transactions can be blocked and reviewed before processing, ensuring on-chain security. As an L2 of the Optimism superchain, Mode stands in the middle ground, connecting human and agent users with the best DeFi ecosystem.
Comparison of Top Blockchains for AI Agents
Solana and Base are undoubtedly the two main chains where most AI agent frameworks and tokens are built and deployed. AI agents leverage Solana's high throughput and low-latency network, as well as the open-source ElizaOS, to deploy agent tokens, while Virtuals serves as a launchpad for deploying agents on Base. Although both have hackathons and funding incentives, they have not yet reached the level of AI initiatives that Mode has achieved.
NEAR previously defined itself as an AI-centric L1 blockchain, featuring an AI task marketplace, an open-source AI agent framework at the NEAR AI Research Center, and NEAR AI assistants. They recently announced a 20 million dollar AI agent fund to scale fully autonomous and verifiable agents on NEAR.
Chainbase
Chainbase provides fully on-chain verifiable structured datasets that enhance AI agents' trading, insights, predictions, alpha-seeking, and more. They launched manuscripts, a blockchain data flow framework for integrating on-chain and off-chain datasets into target data storage for unrestricted querying and analysis.
This enables developers to customize data processing workflows according to their specific needs. By standardizing and processing raw data into clean, compatible formats, they ensure that their datasets meet the stringent requirements of AI systems, reducing preprocessing time while improving model accuracy, helping to create reliable AI agents.
Based on its extensive on-chain data, they also developed a model called Theia that translates on-chain data into user data analytics without any complex coding knowledge. The data utility of Chainbase is evident in their partnerships, where AI protocols are using their data to:
ElizaOS agent plugins for on-chain driven decision-making
Build Vana AI assistants
Flock.io social network intelligence, providing user behavior insights
Theoriq's data analysis and predictions for DeFi
Collaborate with 0G, Aethir, and io.net
Compared to Traditional Data Protocols
Data protocols such as The Graph, Chainlink, and Alchemy provide data, but not in an AI-centric manner. The Graph offers a platform for querying and indexing blockchain data, giving developers access to raw data that is not built for trading or strategy execution. Chainlink provides oracle data feeds but lacks AI-optimized datasets for predictions, while Alchemy primarily offers RPC services.
In contrast, Chainbase data is specifically prepared blockchain data that can be easily utilized by AI applications or agents in a more structured and insightful manner, allowing agents to conveniently access 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 vast amounts of real-time and historical blockchain data, enabling AI agents to operate more effectively.
sqd.ai offers real-time data indexing (including indexing of unconfirmed blocks) at speeds of over 150,000+ blocks per second, faster than any other indexer. In the past 24 hours, they have provided over 11TB of data, meeting the high throughput demands of billions of autonomous AI agents and developers.
https://x.com/helloSQD/status/1879575591118414003
Their customizable data processing platform provides tailored data according to the needs of AI agents, while DuckDB offers efficient data retrieval for local queries. Their comprehensive datasets support over 100 EVM and Substrate networks, including event logs and transaction details, which are invaluable 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. Furthermore, sqd.ai can handle the increasing data load by adding more processing nodes, supporting a growing number of AI agents (estimated to reach billions).
Cookie
Cookie provides a modular data layer for AI agents and clusters, specifically designed for processing social data. It features an AI agent dashboard that tracks top agent sentiments on-chain and across social platforms, and recently launched a plug-and-play data cluster API for other AI agents to detect popular narratives and sentiment shifts in CT.
Their data cluster covers over 7TB of real-time on-chain and social data sources, supported by 20 data agents, providing insights into market sentiment and on-chain analysis. Their latest AI agent @agentcookiefun utilizes 7% of their data cluster capacity, providing market predictions and discovering new opportunities by leveraging various other agents operating beneath it.
Next Steps for DeFAI
Currently, most AI agents in DeFi face significant limitations in achieving full autonomy. For example:
The abstraction layer translates user intentions into execution but often lacks predictive capabilities.
AI agents may generate alpha through analysis but lack independent trade execution.
AI-driven dApps can handle vaults or trades but are passive rather than proactive.
The next phase of DeFAI may focus on integrating useful data layers to develop the optimal agent platform or agents. This will require deep on-chain data regarding whale activities, liquidity changes, and more, while generating useful synthetic data for better predictive analysis, combined with sentiment analysis from the general market, whether it be 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 seamlessly generate and execute trading strategies from a single interface. As these systems mature, we may see future DeFi traders relying on AI agents to autonomously assess, predict, and execute financial strategies with minimal human intervention.
Final Thoughts
Given the significant shrinkage of AI agent tokens and frameworks, some may view DeFAI as a fleeting phenomenon. However, DeFAI is still in its early stages, and the potential for AI agents to enhance the usability and performance of DeFi is undeniable.
The key to unlocking this potential lies in acquiring high-quality real-time data, which will improve AI-driven trading predictions and executions. An increasing number of protocols are integrating different data layers, and data protocols are building plugins for frameworks, highlighting the importance of data in agent decision-making.
Looking ahead, verifiability and privacy will be key challenges that protocols must address. Currently, most AI agent operations remain a black box, requiring users to entrust their funds to them. Therefore, the development of verifiable AI decision-making will help ensure transparency and accountability in agent processes. Integrating protocols based on TEE, FHE, or even zk-proofs can enhance the verifiability of AI agent behavior, thereby fostering trust in autonomy.
Only by successfully combining high-quality data, robust models, and transparent decision-making processes can DeFAI agents achieve widespread adoption.
About DFG
Digital Finance Group (DFG) is a leading global Web3 investment and venture capital firm founded in 2015. DFG manages over $1 billion in assets, with investments spanning various sectors within the blockchain ecosystem. Our portfolio includes over 100 pioneering projects such as Circle, Ledger, Coinlist, Near, Solana, Render Network, and ZetaChain.
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 collaborating with the most transformative and promising blockchain and Web 3.0 projects that are poised to revolutionize the industry.
DFG Website: https://dfg.group
DFG Twitter: @DFG__Official
DFG LinkedIn: DFG
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