The Evolution of Intelligent DeFi: From Automation Tools to AgentFi

In the current cryptocurrency industry, stablecoin payments and Decentralized Finance applications are among the few validated sectors with real demand and long-term value. Meanwhile, various Agents are gradually becoming the practical interface forms in the AI industry, serving as the key intermediate layer connecting AI capabilities and user needs.

In the field of the integration of Crypto and AI, especially in the direction where AI technology feeds back into Crypto applications, current exploration mainly focuses on three typical scenarios:

1. Conversational Interactive Agents: Mainly focused on chatting, companionship, and assistant types. Although most are still wrappers of general large models, due to the low development threshold and natural interaction, combined with token incentives, they have become one of the earliest forms pushed to the market to gain user attention.

2. Information Integration Agent: Focused on the intelligent integration of online and on-chain information. Kaito, AIXBT, and others have achieved success in the field of online but off-chain information search integration, while on-chain data integration is still in the exploratory stage without any obvious leading projects.

3. Strategy Execution Agent: Focused on payments with stablecoins and the execution of DeFi strategies, extending into two main directions: Agent Payment and DeFAI. This type of Agent is more deeply embedded in on-chain trading and asset management logic, promising to break through the speculation bottleneck and create an intelligent execution infrastructure with financial efficiency and sustainable returns.

This article will focus on the evolutionary path of the integration of Decentralized Finance and AI, outlining its development stages from automation to intelligence, and analyzing the infrastructure, scenario space, and key challenges of strategy execution agents.

The Three Stages of DeFi Intelligence: Automation, Copilot, and the Leap to AgentFi

In the evolution of DeFi intelligence, we can divide the system capabilities into three stages: Automation, Intent-Centric Copilot, and AgentFi.

• Automation is more like a Rule Trigger: it executes fixed tasks based on preset conditions, such as arbitrage, rebalancing, and take profit/stop loss, and cannot generate strategies or operate independently.

• Copilot introduces intent recognition and semantic parsing capabilities, allowing users to input natural language. The system understands, breaks down, and suggests execution paths, but ultimately requires user confirmation, and the execution chain is not closed.

• AgentFi represents a complete "perception → reasoning/strategy generation → on-chain execution → evolution" intelligent closed loop, which is an intelligent agent (Agent) with on-chain autonomous execution and continuous evolution capabilities.

| Dimension | Automated Infra | Intent-Centric Copilot | AgentFi | |------|-------------|------------------------|---------| | Core Logic | Rule Trigger + Condition Execution | Intent Recognition + Action Guidance | Strategy Closure + Autonomous Execution | | Execution Method | Trigger execution based on preset conditions (if-then) | Understand user instructions and assist in breaking down operations | Fully autonomous perception, judgment, execution | | User Interaction | No interaction required, passive trigger execution | Users express intentions through prompts, the system assists in decomposition | No human interaction required, can collaborate with people/Agents | | Smart Level | Low, Process Automation | Medium, Interactive Understanding | High, Autonomous Strategy Generation and Evolution | | Strategy Capability | None, executes preset tasks | Limited, relies on user instructions | Strong, can self-learn and optimize combinations | | Difficulty of Implementation | Low, more backend services | Medium, requires strong frontend interaction design | High, requires deep collaboration with AI/execution infrastructure | | On-chain Execution | ✅ Perception ❌ Decision ( Fixed Rule Trigger ) ✅ Support Simple Execution | ✅ Perception ✅ Decision ⚠️ Execution Requires User Confirmation | ✅ Perception ✅ Decision ✅ Complete Closed-loop On-chain Execution | | Typical Representatives | Gelato, Mimic | HeyElsa.ai, Bankr | Giza ARMA |

To determine whether a project truly belongs to AgentFi, it is necessary to see if it meets at least three of the following five core criteria:

1. Autonomous perception of on-chain status/market signals (not static inputs, but real-time monitoring)
2. Possess the ability to generate and combine strategies (not preset strategies, but able to formulate action plans based on context)
3. Can autonomously execute operations on the chain (no user interaction required, able to perform complex operations such as swap/lend/stake)
4. Having persistent state and evolutionary capabilities (Agents have a lifecycle, can run for a long time and self-adjust based on feedback)
5. Equipped with Agent-Native architecture (such as dedicated Agent SDK, managed execution environment, Agent middleware, etc.)

In other words, automated trading ≠ Copilot, nor ≠ AgentFi: automated trading is merely a "rule trigger"; Copilot can understand user intent and provide operational suggestions, but still relies on human participation; whereas a true AgentFi is an "intelligent agent with perception, reasoning, and on-chain autonomous execution capabilities" that can complete strategy loops and continuously evolve without human intervention.

Decentralized Finance Scenario Intelligent Adaptability Analysis

In the DeFi (Decentralized Finance) ecosystem, the core application scenarios can be roughly divided into asset circulation and exchange types, and yield-generating finance types. We believe that there are significant differences in the adaptability of these two types of scenarios on the intelligent path:

1. Asset circulation and exchange scenarios

Asset circulation and exchange scenarios are primarily based on atomic interactions, including Swap trading, cross-chain bridges, and fiat deposits and withdrawals. Their essential characteristics are "intent-driven + single atomic interaction". The trading process does not involve profit strategies, state maintenance, and evolution logic, and is mostly suitable for the lightweight execution path of Intent-Centric Copilot, thus not belonging to AgentFi.

Due to its lower engineering threshold and simple interaction, most DeFi AI projects in the market are currently at this stage, which do not constitute a closed-loop intelligent AgentFi; however, for a few advanced complex Swap strategies (such as cross-asset arbitrage, perpetual hedge LP, leverage rebalancing, etc.), the capability of AI Agents is actually needed, which is still in the early exploration stage.

| Scene Category | Continuous Revenue | AgentFi Compatibility | Implementation Difficulty | Description | |---------|------------|--------------|------------|------| | Swap Transaction | ❌ No | ⚠️ Partial Compatibility (Only Intent transactions are not true AgentFi) | ✅ Easy to Implement | Single atomic operation (such as swapping coins), no strategy state accumulation, suitable for Copilot calls. | Cross-Chain Bridge | ❌ No | ❌ Weak | ✅ Easy to Implement | Cross-chain is an intermediary transmission that does not involve strategy planning and adjustments, and AI participation is minimal. | Fiat Deposit and Withdrawal | ❌ No | ❌ None | ❌ Uncontrollable | Highly reliant on CeFi channels and compliance processes, on-chain agents cannot autonomously initiate operations | | Aggregation Optimization | ⚠️ Not Guaranteed | ⚠️ Partial Compatibility | ✅ Moderate | Primarily based on automation tools. If it can combine quotes from multiple platforms or maximize yield paths, it can be executed by lightweight Agents, but it is difficult for long-term evolution into intelligent agents | | ✅ Swap Trading Combinations | ✅ Potential for Profit | ✅ Immature | ❌ Difficult to Implement | Such as cross-asset arbitrage, perpetual hedge LP, dynamic position adjustment, etc., require complex strategy engines for support, currently still in the prototype stage with no available Agent |

2. Asset Income Financial Scenarios

Asset income financial scenarios have clear income objectives, a complex strategy combination space, and dynamic state management requirements, which naturally align with AgentFi's "strategy closed-loop + autonomous execution" model. Its core features are as follows:

• Quantifiable return targets (APR / APY) facilitate the Agent in establishing optimization functions;
• The strategy portfolio space is vast, covering multiple assets, multiple timeframes, multiple platforms, and multiple interaction processes;
• Operations require frequent management and real-time adjustments, suitable for execution and maintenance by on-chain agents.

| Rank | Scene Category | Continuous Income | AgentFi Adaptability | Engineering Difficulty | Description | |-----|---------|------------|--------------|---------|------| | 1 | Liquidity Mining | ✅ Yes | ✅✅✅ Very High | ❌ High | Strategy requires frequent dynamic adjustments (such as reinvestment, migration, dual pool strategies, etc.), best suited for deploying AI strategy agents | | 2 | Lending | ✅ Yes | ✅✅✅ Very High | ✅ Low | Interest rate fluctuations + collateral status readable, risk warnings and automatic rebalancing easy to achieve | | 3 | Pendle (PT/YT Yield Rights Trading) | ✅ Yes | ✅✅ High | ❌ High | The yield duration and structure are diverse, and the combination trading is complex. Smart agents can optimize trading timing and yield stability | | 4 | Funding Rate Arbitrage (Perp/CeFi/DeFi Hybrid) | ✅ Yes | ✅✅ High | ❌ Very High | Multi-market arbitrage has AI advantages, but the complexity of cross-chain interaction and collaboration is extremely high, still in the exploration stage | | 5 | Staking / Restaking / LRT Strategy Combination | ⚠️ Fixed Income | ⚠️ Conditional Adaptation | ⚠️ Medium | Static staking is not suitable for Agents, but dynamic combinations of multiple LST + Lending + LP, etc. can be intervened by agents | | 6 | RWA (Real World Assets) | ⚠️ Stable Returns | ❌ Low | ⚠️ High Compliance | Stable return structure, high compliance threshold, no interconnectivity between protocols, no space for AgentFi strategy implementation in the short term |

Due to multiple factors such as the constraints of yield duration, volatility frequency, complexity of on-chain data, difficulty of cross-protocol integration, and compliance restrictions, there are significant differences in the adaptability and engineering feasibility of different yield scenarios in the AgentFi dimension. The priority recommendations are as follows:

High Priority Business Landing Direction:

• Lending / Borrowing: Interest rate fluctuations are easy to track with standardized execution logic, suitable for lightweight agents.
• Yield Farming: The liquidity pools are dynamic and frequently changing, with a wide range of strategy combinations and high yield fluctuations. AgentFi can significantly optimize annualized returns and interaction efficiency, but engineering implementation poses certain challenges.

Medium to long-term exploration layout direction:

• Pendle yield right trading: The time dimension and yield curve are clear, suitable for Agent management of maturity rotation and inter-pool arbitrage;
• Funding Rate Arbitrage: Theoretical returns are considerable, but challenges in cross-market execution and off-chain interactions need to be addressed, and the engineering difficulty is high;
• LRT Dynamic Combination Structure: Static staking is not suitable, you can try strategies such as LRT + LP + Lending for automatic adjustment.
• RWA multi-asset portfolio management: difficult to implement in the short term, Agent can assist with portfolio optimization and maturity strategy;

Introduction of Smart Projects in DeFi Scenarios

1. Automation Tools ( Automation Infra ): Rule Triggering and Condition Execution

Gelato is one of the earliest infrastructures for DeFi automation, having previously provided condition-triggered task execution support for protocols like Aave and Reflexer, but it has now transitioned to being a Rollup as a Service provider. Currently, the main battleground for on-chain automation has also shifted to DeFi asset management platforms (DeFi Saver, Instadapp). These platforms integrate standardized automated execution modules, including Limit Order settings, liquidation protection, automatic rebalancing, DCA, grid strategies, and more. Additionally, we are seeing some more complex DeFi automation tool platform projects:

Mimic.fi

Mimic.fi is an on-chain automation platform that serves DeFi developers and project parties, supporting the construction of programmable automation tasks on chains such as Arbitrum, Base, and Optimism. Its core automatically executes cross-protocol operations through "if-then" rule triggers, with the architecture divided into three layers: Planning (task and trigger definition), Execution (intent broadcasting and execution bidding), and Security (triple verification and security control). Currently, it adopts an SDK integration method, and the product is still in the early stages of deployment.