DeFAI: How AI is Unlocking DeFi’s Potential
What is DeFAI
Decentralized Finance (DeFi) has been a core pillar of 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 that even seasoned users struggle to navigate the vast number 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-driven focus in 2024. This transition has given rise to DeFi AI (DeFAI) — an emerging sector where AI enhances DeFi through automation, risk management, and capital optimization.
DeFAI operates across several layers. The blockchain serves as the foundational layer, as AI agents must interact with specific chains to execute transactions and enforce smart contracts. Above this, data and compute layers provide the necessary infrastructure for training AI models, drawing from historical price data, market sentiment, and on-chain analytics. Privacy and verifiability layers ensure that sensitive financial data remains secure while maintaining trustless execution. Finally, agentic frameworks allow developers to build specialized AI-driven applications, such as autonomous trading bots, credit risk assessors, and on-chain governance optimizers.
Although this ecosystem mapping can be further expanded, these are the top categories of projects being built on DeFAI.
While the DeFAI ecosystem continues to expand, the most prominent projects can be grouped into three primary categories:
1. Abstraction Layer
The protocols building on this category act as a ChatGPT-like user-friendly interface for DeFi, allowing users to input prompts for on-chain execution. They are often integrated with multiple chains and dApps, and execute user intents while eliminating manual steps in complex transactions.
Some features these protocols can execute include:
- Swap, bridge, lend/withdraw, execute transactions across chains
- Copy trade wallets or Twitter/X profiles
- Automate trades such as take profit / stop loss based on % of their position size
For example, instead of manually withdrawing ETH from Aave, bridging it to Solana, swapping for SOL/Fartcoin, and providing liquidity on Raydium — abstraction layer protocols do it in one step.
Key Protocols:
- @griffaindotcom — Network of agents to execute transactions for users
- @HeyAnonai — Protocol that processes user prompts for DeFi transactions & real-time insights
- @orbitcryptoai — AI companion for DeFi interaction
2. Autonomous Trading Agents
Unlike traditional trading bots that follow pre-set 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 refine strategies over time
- Predict market movements for better long/short decisions
- Execute complex DeFi strategies like basis trading
Key Protocols:
- @Almanak__ — Platform for training, optimizing and deploying autonomous financial agents
- @Cod3xOrg — Launching AI agents that execute financial tasks on the blockchain
- @Spectral_Labs — Network for creating autonomous on-chain trading agents
3. AI Powered DApps
DeFi dApps offer features such as lending, swapping, yield farming and more. AI and AI agents can enhance these services by:
- Optimizing liquidity provision by rebalancing LP positions for better APY
- Scanning tokens for risk by detecting potential rugs or honeypots
Key Protocols:
- ARMA by @gizatechxyz — AI agent optimizing USDC yield across Mode and Base
- @SturdyFinance — AI-powered yield bearing vaults
- @derivexyz — Options & perps platform optimized with an intelligent AI copilot
Key Challenges
Top protocols building on these layers face a few challenges:
- These protocols depend on real-time data feeds for optimal transaction execution. Poor data quality could lead to inefficient routes, failed transactions, or unprofitable trades
- AI models rely on historical data, but crypto markets are highly volatile. Agents must be trained on diverse, high-quality datasets to remain effective
- A holistic overview of asset correlations, liquidity shifts, and market sentiment is required to access the overall market condition
Protocols building on these categories have been well-received by the market. However, to deliver an even better product and optimal results, they should consider integrating a variety of different quality datasets to bring their products to the next level.
The Data Layer — Powering DeFAI’s Intelligence
AI is only as good as the data it relies on. For AI agents to work effectively in DeFAI, they need real-time, structured, and verifiable data. For example, abstraction layers require access to data on-chain through RPCs and APIs to social networks while trading and yield-optimizing agents require data to further refine their trading strategy and reallocate resources.
Quality datasets give agents better capability to make predictive analyses of future price action, providing recommendations for trades of serving the bias for their long or short positions for certain assets.
Key Data Providers in DeFAI
Mode Synth Subnet
As Bittensor’s 50th subnet, Synth creates synthetic data for financial forecasting abilities on agents. Compared to other traditional price prediction systems, Synth captures the full distribution of price movements & their associated probabilities, to build the most accurate synthetic data in the world for powering Agents and LLMs.
Providing more high-quality datasets can empower AI agents to make better directional decisions in trading while predicting APY fluctuations in different market conditions for liquidity pools to reallocate or withdraw liquidity whenever required. Since mainnet launch, they have been a strong demand by DeFi teams to integrate Synth’s data via their API.
The Most Focused AI Agentic Blockchain
Besides building a data layer for AI and agents, Mode has also positioned themselves as a blockchain that is building for the full stack in the future of DeFAI. They have recently deployed Mode Terminal, a DeFAI co-pilot for executing on-chain transactions via user prompts which is available soon for $MODE stakers.
Furthermore, Mode has been supporting many teams that are building on AI and agents. Considerable effort has been made to integrate protocols such as Autonolas, Giza, Sturdy, and many more into Mode’s ecosystem, growing quickly as more agents are developed and executing transactions.
These initiatives have been accomplished all while they’re upgrading their network with AI, most notably with an AI-secured sequencer for their blockchain. By using simulations and AI to analyze transactions before execution, high-risk transactions can be blocked and reviewed before processing to ensure safety on-chain. As an L2 on the Optimism Superchain, Mode stands at the middle ground connecting human and agentic users to the best of DeFi ecosystems.
Comparing top blockchains AI agents are built on
Solana and Base have undoubtedly been the 2 major chains for the majority of AI agentic frameworks and tokens to build and launch on. AI agents have leveraged Solana’s high throughput and low latency network with the open source ElizaOS for the deployment of agentic tokens, while Virtuals served as a launchpad for deploying agents on Base. Although both of them have hackathons and fund incentives, they have not reached the level Mode has achieved in terms of their AI initiatives as a chain.
NEAR previously defined themselves as an AI-centric layer 1 blockchain with features such as their AI task marketplace, NEAR AI Research Hub with open source AI agentic framework and NEAR AI Assistant. They recently announced their $20 million AI agent fund for scaling fully autonomous and verifiable agents on NEAR.
Chainbase
Chainbase provides omnichain verifiable on-chain structured datasets that enhance AI agents for trading, insight, prediction, alpha sourcing, and more. They introduced manuscripts, a blockchain data streaming framework for integrating on and off-chain data into target data storage for unrestricted querying and analysis.
This allows developers to customize data processing workflows according to their specific needs. The standardization and processing of raw data into clean, compatible formats ensures their datasets meet the stringent requirements of AI systems to reduce preprocessing time while improving model accuracy to help create reliable AI agents.
Building on their expansive on-chain data, they also developed a model named Theia that interprets on-chain data into data analysis for users without the need for any complex coding knowledge. Chainbase’s data usefulness is evident in their partnerships, where AI protocols are using their data for:
- ElizaOS agent plugin for on-chain driven decision making
- Building Vana AI assistants
- Flock.io social network intelligence for user behavior insights
- Theoriq’s data analysis & prediction for DeFi
- Also partnered with 0G, Aethir and io.net
Compared to Traditional Data Protocols
Data protocols such as The Graph, Chainlink, and Alchemy provide data but are not AI-focused. The Graph provides a platform for querying and indexing blockchain data, providing raw data access for developers that is not structured for trading or strategy execution. Chainlink provides oracle data feeds but lacks AI-optimized datasets for prediction while Alchemy primarily offers RPC services.
In contrast, Chainbase data is specifically prepared blockchain data that can be readily consumed by AI applications or agents in a more structured and insight-ready format, making it much more convenient for agents to obtain relevant data relating to market, liquidity, and token data on-chain.
sqd.ai
sqd.ai, formerly known as Subsquid, is developing an open database network tailored for AI agents and Web3 services. Their decentralized data lake offers permissionless, cost-efficient access to vast amounts of real-time and historical blockchain data, enabling AI agents to operate more effectively.
Offering real-time data indexing including those of unfinalized blocks, sqd.ai provides indexing speeds of up to 150k+ blocks per second which is faster than any other indexer. Serving over 11TB of data in the past 24 hours, they cater to the high-throughput demands of billions of autonomous AI agents and developers.
Their customizable data processing platform enables tailored data for AI agents’ needs while DuckDB provides efficient data retrieval for local querying. Supporting over 100 EVM and Substrate networks, their comprehensive data sets include event logs and transaction details which are invaluable for AI agents operating across multiple blockchains.
The incorporation of zk-proofs ensures that AI agents can access and process sensitive data without compromising privacy. Additionally, sqd.ai can handle increasing data loads by adding more processing nodes, supporting the growing number of AI agents that is estimated to reach billions.
Cookie
Cookie provides a modular data layer for AI agents and swarms, specifically catering to social data. It features an AI agent dashboard tracking top agent mindshare on-chain and on social platforms, and has recently launched a plug-in-and-play data swarm API for other AI agents to detect trending narratives and mindshare shifts in CT.
Their data swarm covers over 7TB of real-time on-chain and social data feeds powered by 20 data agents, providing insights into both market sentiment and analytics on-chain. Their newest AI agent @agentcookiefun utilizes their data swarm at 7% capacity, providing market predictions and identifying new opportunities by utilizing a variety of other agents operating under it.
The Next Step in DeFAI
Currently, most AI agents in DeFi face significant limitations in achieving full autonomy. For example:
- Abstraction layers convert user intent into execution but often lack predictive capabilities
- AI agents may generate alpha through analysis but lack independent trade execution
- AI-powered dApps can handle vaults or trades but are reactive instead of proactive
The next phase of DeFAI will likely focus on integrating useful data layers for developing the best agentic platforms or agents. This will require insightful on-chain data regarding whale movements, liquidity shifts, and more, while churning useful synthetic data for better predictive analysis, combined with sentiment analysis from the general market be it in token fluctuations in a certain category (like AI agents, DeSci, etc) or on social networks.
The ultimate end game is AI agents capable of seamlessly generating and executing trading strategies from a single interface. As these systems mature, we may see a future where DeFi traders rely on AI agents to autonomously assess, predict, and execute financial strategies with minimal human intervention.
Final Thoughts
Some may view DeFAI as a fleeting trend, given the significant drawdowns in AI agent tokens and frameworks. However, it remains in its early stages, and the potential for AI agents to enhance DeFi’s usability and performance is undeniable.
The key to unlocking this potential lies in access to high-quality, real-time data, which will improve AI-driven trade prediction and execution. There are an increasing number of protocols integrating different data layers and data protocols building plug-ins for frameworks which highlights the importance of data to agent decisions.
Moving forward, verifiability and privacy will be key challenges that protocols must address. Currently, most AI agent operations remain a black box which users have to trust their funds with. Thus, the development of verifiable AI decision-making will help ensure transparency and accountability in agentic processes. Integrating protocols building on TEE, FHE, and even zk-proofs can all enhance the verifiability of AI agents' actions in the move toward trust in autonomy.
DeFAI agents will only achieve widespread adoption when they successfully combine quality data, robust models, and transparent decision-making processes.
About DFG
Digital Finance Group (DFG) is a leading global Web3 investment and venture firm, established in 2015. With assets under management exceeding $1 billion, DFG’s investments span across diverse sectors within the blockchain ecosystem. Our portfolio boasts investments in pioneering projects such as Circle, Ledger, Coinlist, Near, Solana, Render Network, ZetaChain, and over 100 others.
At DFG, we are committed to generating value for our portfolio companies through market research, strategic consult, and sharing of our vast resources globally. We are actively working with the most transformative and promising blockchain and Web 3.0 projects poised to revolutionize the industry.
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