DeFAI Full Explanation: How does AI unleash the potential of DeFi?

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, making it difficult for even experienced users to navigate the vast number of chains, assets, and protocols.

Meanwhile, artificial intelligence (AI) has evolved from a broad, basic 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 base 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 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 interface similar to ChatGPT for DeFi, allowing users to enter prompts for on-chain execution. They are often integrated 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:

Swap, cross-chain, lend/withdraw, cross-chain execution of transactions

Copy trading wallets or Twitter/X profiles

Automatically execute transactions such as stop-profit/stop-loss based on position size percentages

For example, there is no need to manually withdraw ETH from Aave, cross-chain it to Solana, exchange SOL/Fartcoin, and provide liquidity on Raydium - the abstraction layer protocol can do it in one step.

Main protocols:

@griffaindotcom — A network of agents that execute trades for users

@HeyAnonai — A protocol that handles user alerts for DeFi trades and real-time insights

@orbitcryptoai — An AI companion for DeFi interactions

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 improve strategies

Predict market movements to make better long/short decisions

Execute complex DeFi strategies like basic trading

Main protocols:

@Almanak__ — A platform for training, optimizing, and deploying autonomous financial agents

@Cod3xOrg — Introducing AI agents that perform financial tasks on the blockchain

@Spectral_Labs — Creating a network of autonomous on-chain trading agents

3. AI-driven DApps

DeFi dApps provide features such as lending, swapping, yield farming, etc. 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

Major protocols:

@gizatechxyz’s ARMA — AI agent for optimizing USDC yield in Mode and Base

@SturdyFinance — AI-driven yield vault

@derivexyz — Optimized options and perpetual contracts platform using smart AI co-pilot

Major challenges

Top 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 trades

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 conditions

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 a variety of datasets of varying quality 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 better perform predictive analysis on future price behavior, providing trade recommendations to suit their preferences for long or short positions on certain assets.

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 full distribution of price changes and their associated probabilities, thereby building the world's most accurate synthetic data to power 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 so that liquidity pools can reallocate or extract liquidity when needed. Since the launch of the mainnet, they have been having strong requests from DeFi teams to integrate Synth's data through their API.

Most Watched AI Agent Blockchains

In addition to building a data layer for AI and agents, Mode is also positioning itself to build a full-stack blockchain for DeFAI's future. They recently deployed Mode Terminal, a co-pilot for DeFAI to execute on-chain transactions through user prompts, which will soon be available to $MODE stakers.

In addition, Mode also supports many AI and agent-based teams. Mode has made great efforts to integrate protocols such as Autonolas, Giza, Sturdy into its ecosystem, and Mode has grown rapidly as more agents are developed and execute transactions.

These initiatives have been achieved while they have upgraded 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 the 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 on which AI agents are based

Solana and Base are undoubtedly the two main chains where most AI agent frameworks and tokens are built and released. AI agents leverage 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 achieved by Mode in terms of their AI plans as a chain.

NEAR previously defined itself as an AI-centric L1 blockchain, with features including an AI task market, 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 expand fully autonomous and verifiable agents on NEAR.

Chainbase

Chainbase provides fully verifiable on-chain structured datasets that enhance AI agents’ trading, insights, predictions, alpha-finding, and more. 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 that translates on-chain data into data analysis for users without any complex coding knowledge. Chainbase’s data utility is evident in their partnerships, where AI protocols are using their data to:

ElizaOS agent plugin for on-chain driven decision making

Building Vana AI assistant

Flock.io social network intelligence for user behavior insights

Theoriq data analysis and predictions for DeFi

Also working 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-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 strategy 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

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 run more efficiently.

sqd.ai provides real-time data indexing (including indexing of outstanding blocks) at up to 150,000+ blocks per second, faster than any other indexer. In the past 24 hours, they have served over 11TB of data, meeting the high-throughput needs of billions of autonomous AI agents and developers.

Their customizable data processing platform delivers customized data based on the needs of AI agents, while DuckDB provides efficient data retrieval for local queries. Their comprehensive dataset supports over 100 EVM and Substrate networks, including event logs and transaction details, which is extremely valuable for AI agents running across multiple blockchains.

The inclusion of zero-knowledge proofs ensures that AI agents can access and process sensitive data without compromising privacy. Additionally, sqd.ai can support a 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 mindset on-chain and on social platforms, and recently launched a plug-and-play data cluster API for other AI agents to detect popular narratives and mindset 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 market sentiment and on-chain analytics. Their latest AI agent @agentcookiefun leverages their data swarm at 7% capacity, providing market predictions 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:

Abstract layers translate user intent into execution, but often lack predictive capabilities

AI agents may generate alpha through analytics, but lack independent trade execution

AI-driven dApps can handle vaults or trades, but are reactive rather than proactive

The next phase of DeFAI will likely focus on integrating useful data layers to develop an optimal agent platform or agent. This will require deep on-chain data about whale activity, liquidity changes, etc., while generating 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 of AI agent tokens and frameworks, some may think that DeFAI is just a flash in the pan. However, DeFAI is still in its early stages, and the potential of AI agents to enhance DeFi 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. More and more protocols are integrating different data layers, and data protocols are building plugins for the framework, which highlights the importance of data for agent decision-making.

Going forward, verifiability and privacy will become key challenges that protocols must address. Currently, most AI agent operations are still 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, and even zk-proofs-based protocols can enhance the verifiability of AI agent behavior, thereby achieving trust in autonomy.

DeFAI agents can only gain widespread adoption if they successfully combine high-quality data, robust models, and transparent decision-making processes.