The Evolution of Intelligent DeFi: From Automated Tools to AgentFi

In the current cryptocurrency industry, stablecoin payments and Decentralized Finance applications are among the few sectors that have been proven to possess real demand and long-term value. At the same time, the flourishing Agents are gradually becoming the practical implementation form of user interfaces in the AI industry, serving as a key intermediary layer that connects AI capabilities with 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 explorations mainly focus on three typical scenarios:

1. Conversational Interactive Agent: Mainly focused on chat, companionship, and assistant roles. Although most are still shells of general large models, due to low development thresholds and natural interactions, combined with token incentives, they have become the first form pushed to the market to gain user attention.
2. Information Integration Agent: Focuses 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 with no明显跑出项目.
3. Strategy Execution Agent: Focusing on stablecoin payment and DeFi strategy execution, the two major directions of Agent Payment and DeFAI are extended. Such agents are more deeply embedded in on-chain trading and asset management logic, which is expected to break through the bottleneck of speculation and form 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.

Decentralized Finance Intelligent Three Stages: Automation, Copilot and AgentFi's Leap

In the evolution of smart DeFi, we can divide 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, take profit and 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, decomposes, and suggests execution paths, but ultimately requires user confirmation; the execution chain is not closed.
• AgentFi represents a complete "perception → reasoning/strategy generation → on-chain execution → evolution" intelligent loop, and 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 Loop + Autonomous Execution | | Execution Method | Trigger execution based on preset conditions (if-then) | Understand user instructions, assist in breaking down operations | Fully autonomous perception, judgment, execution | | User Interaction | No interaction required, passive trigger executed | User expresses intent through prompt, system assists in breakdown | No human interaction needed, can collaborate with person/Agent | | Intelligence 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 | | Implementation Difficulty | Low, mainly 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 needs to meet 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-chain (no user interaction required, able to perform complex operations such as swap/lend/stake)
4. Has persistent state and evolutionary capability (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, and even more ≠ AgentFi: automated trading is merely a "rule trigger", while Copilot can understand user intent and provide operational suggestions, but still relies on human participation; the real AgentFi is an "intelligent agent with perception, reasoning, and on-chain autonomous execution capabilities", which can complete strategy loops and continuous evolution without human intervention.

Decentralized Finance Scenario Intelligent Adaptability Analysis

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

1. Asset Circulation and Exchange Scenarios

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

Due to its low engineering threshold and simple interaction, most DeFi AI projects on the market are currently at this stage, which does not constitute a closed-loop intelligent agent for AgentFi; however, a few advanced complex Swap strategies (such as cross-asset arbitrage, perpetual hedging LP, leveraged rebalancing, etc.) actually require the capabilities of an AI Agent for integration, which is still in the early exploration stage.

| Scenario Category | Continuous Earnings | AgentFi Compatibility | Implementation Difficulty | Description | |----------------|------------|-------------------------------|------------|----------------------------------------------------| | Swap Trading | ❌ No | ⚠️ Partially compatible (only Intent trading is not true AgentFi) | ✅ Easy to implement | Single atomic operation (e.g., currency exchange), no strategy state accumulation, suitable for Copilot calls. | | Cross-Chain Bridge | ❌ No | ❌ Weak | ✅ Easy to Implement | Cross-chain is an intermediary transmission, not involving strategic planning and adjustment, with very low AI participation. | Fiat Deposit and Withdrawal | ❌ No | ❌ None | ❌ Uncontrollable | Highly dependent on CeFi channels and compliance processes, on-chain Agent cannot autonomously initiate operations | | Aggregation Optimization | ⚠️ Not guaranteed | ⚠️ Partially compatible | ✅ Moderate | Mainly based on automation tools, if multiple platform quotes or yield maximization paths can be combined, it can be executed by a lightweight Agent, but it's difficult for long-term evolution of the agent. | ✅ Swap Trading Combinations | ✅ Potential for Profit | ✅ Not Mature | ❌ Difficult to Implement | Such as cross-asset arbitrage, perpetual hedge LP, dynamic position adjustment, etc., require complex strategy engine support, currently still in the prototype stage with no available Agents |

2. Asset Income Financial Scenarios

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

• Quantifiable yield targets (APR / APY) facilitate Agents 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 | Scenario Category | Continuous Income | AgentFi Compatibility | Engineering Difficulty | Description | |--------|------------------------------------|------------|-----------------|----------|---------------------------------------------| | 1 | Liquidity Mining | ✅ Yes | ✅✅✅ Very High | ❌ High | Strategies require frequent dynamic adjustments (such as reinvestment, migration, dual pool strategies, etc.), most suitable for deploying AI strategy agents | | 2 | Lending | ✅ Yes | ✅✅✅ Very High | ✅ Low | Interest rate fluctuations + collateral status readable, risk warning and automatic rebalancing easy to achieve | | 3 | Pendle (PT/YT Yield Rights Trading) | ✅ Yes | ✅✅ High | ❌ High | Diverse yield terms and structures, complex combination trading, agents can optimize trading timing and yield stability | | 4 | Funding Rate Arbitrage (Perp/CeFi/Decentralized Finance Mixed) | ✅ Yes | ✅✅ High | ❌ Very High | Multi-market arbitrage has AI advantages, but the complexity of off-chain interactions and coordination is extremely high, and it is 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 can be engaged by agents | | 6 | RWA (Real World Assets) | ⚠️ Stable Returns | ❌ Low | ⚠️ Heavy Compliance | Stable return structure, high compliance threshold, no interoperability between protocols, no short-term space for AgentFi strategy implementation |

Due to multiple factors such as the constraints of yield duration, volatility frequency, on-chain data complexity, cross-protocol integration difficulty, and compliance limitations, 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 frequent, with a large strategy combination space and high yield fluctuations. AgentFi can significantly optimize annualized returns and interaction efficiency, but the engineering implementation poses certain challenges;

Long-term layout directions to explore:

• Pendle yield rights 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 like LRT + LP + Lending for automatic adjustment.
• RWA Multi-Asset Portfolio Management: Difficult to implement in the short term, the Agent can provide assistance in portfolio optimization and maturity strategies;

Introduction to Smart Projects in Decentralized Finance Scenarios

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

Gelato is one of the earliest infrastructures for DeFi automation, having previously provided conditional task execution support for protocols like Aave and Reflexer, but it has now transformed into a Rollup as a Service provider. Currently, the main battlefield 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 setting, liquidation protection, automatic rebalancing, DCA, grid strategies, and more. Additionally, we see some more complex DeFi automation tool platform projects:

Mimic.fi

Mimic.fi is an on-chain automation platform.