The Evolution of DeFi Intelligence: From Automated Tools to AgentFi Agents

In the current cryptocurrency industry, stablecoin payments and Decentralized Finance applications are among the few validated sectors with real demand and long-term value. At the same time, the flourishing Agents are gradually becoming the practical implementation of user interfaces in the AI industry, serving as the crucial intermediary layer that connects AI capabilities with user needs.

In the field of the fusion of Crypto and AI, especially in the direction of AI technology feeding back into Crypto applications, current explorations mainly focus on three typical scenarios:

1. Conversational Interactive Agents: Primarily focused on chat, companionship, and assistant roles. Although most are still wrappers around general large models, their low development threshold and natural interaction, combined with token incentives, have made them one of the first forms pushed to the market to attract 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 and integration, while on-chain data integration is still in the exploratory stage with no obvious standout projects.

3. Strategy Execution Agent: Focused on stablecoin payments and the execution of DeFi strategies, it extends into two main directions: Agent Payment and DeFAI. Such agents are more deeply embedded in on-chain trading and asset management logic, promising to break through speculation bottlenecks and form a smart 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 Intelligent DeFi: Automation, Copilot, and the Leap to AgentFi

In the evolution of intelligent DeFi, 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, take profit and stop loss, and cannot generate strategies or operate independently.
• The Copilot introduces intent recognition and semantic parsing capabilities. Users input natural language, and the system understands, breaks down, and suggests execution paths, but ultimately requires user confirmation, making the execution chain not closed.
• AgentFi represents a complete "perception → reasoning/strategy generation → on-chain execution → evolution" intelligent closed loop, which is an agent equipped 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 execution | Users express intentions through prompts, and the system assists in breakdown | No human interaction necessary, can collaborate with humans/Agents | | Intelligence Level | Low, Process Automation | Medium, Interactive Understanding | High, Autonomous Strategy Generation and Evolution | | Strategic Ability | None, executes preset tasks | Limited, relies on user instructions | Strong, can self-learn and optimize combinations | | Difficulty of Implementation | Low, mainly backend services | Medium, requires strong frontend interaction design | High, requires deep collaboration of AI/execution infrastructure | | On-chain execution | ✅ Perception ❌ Decision ( Fixed rule trigger ) ✅ Supports 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 the ability to self-develop action plans based on context)
3. Can autonomously execute operations on the chain (no user interaction required, able to perform complex operations like swap/lend/stake, etc.)
4. Has persistent state and evolutionary ability (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, hosted execution environment, Agent middleware, etc.)

In other words, automated trading ≠ Copilot, and even more ≠ AgentFi: automated trading is merely a "rule trigger"; although Copilot can understand user intentions and provide operational suggestions, it still relies on human participation; while the true AgentFi is an "intelligent agent capable of perception, reasoning, and autonomous execution on-chain," able to complete strategy loops and continuously evolve without human intervention.

Analysis of Intelligent Adaptability in DeFi Scenarios

In the DeFi (Decentralized Finance) system, the core application scenarios can be roughly divided into asset circulation and exchange types and yield 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 transactions, cross-chain bridges, and fiat deposits and withdrawals. Their essential characteristics are "intention-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, not belonging to AgentFi.

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

| Scene Category | Continuous Income | AgentFi Compatibility | Implementation Difficulty | Description | |----------------|------------|-------------------------------|------------|----------------------------------------------------| | Swap Trading | ❌ No | ⚠️ Partial Compatibility (only Intent trading is not real AgentFi) | ✅ Easy Implementation | 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, which does not involve strategy planning and adjustments, 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 | ⚠️ Partial Compatibility | ✅ Moderate | Primarily based on automation tools. If multiple platform quotes or yield maximization paths can be combined, it can be executed by a lightweight Agent, but long-term evolution into an intelligent agent is difficult. | ✅Swap trading combinations | ✅Potential for profits | ✅Immature | ❌Difficult to implement | For cross-asset arbitrage, perpetual hedge LP, dynamic position adjustment, etc., complex strategy engines are needed for support, and it is currently in the prototype stage with no available Agent |

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 the Agent in establishing optimization functions;
• The strategy portfolio is extensive, covering multiple assets, multiple time frames, 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.), best suited for deploying AI strategy agents | | 2 | Lending | ✅Yes | ✅✅✅Very High | ✅Low | Interest rate fluctuations + collateral status readable, risk warning and automatic adjustment easily achievable | | 3 | Pendle (PT/YT Yield Rights Trading) | ✅Yes | ✅✅High | ❌High | The yield term and structure are diverse, combination trading is complex, and the smart agent can optimize the buying and selling timing as well as yield stability | | 4 | Funding Rate Arbitrage (Perp/CeFi/DeFi Mixed) | ✅Yes | ✅✅High | ❌Very High | Multi-market arbitrage has AI advantages, but cross-chain interaction and collaboration complexity 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 such as multiple LST + Lending + LP can be involved. | 6 | RWA (Real World Assets) | ⚠️Stable Returns | ❌Low | ⚠️High Compliance | Stable return structure, high compliance threshold, non-interoperable between protocols, no space for AgentFi strategy implementation in the short term |

Due to multiple factors such as the limitation 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 context of AgentFi. The suggested priorities 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 large strategy combination space and high yield fluctuations. AgentFi can significantly optimize annualized returns and interaction efficiency, but implementing the engineering poses certain challenges;

Medium to long-term exploration layout direction:

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

Introduction to Intelligent 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 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 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 setting, liquidation protection, automatic rebalancing, DCA, grid strategies, etc. In addition, we see some more complex DeFi automation tool platform projects:

Mimic.fi

Mimic.fi is an on-chain automation platform that serves DeFi developers and project teams, supporting the construction of programmable automation tasks on chains such as Arbitrum, Base, and Optimism. Its core functionality is achieved through "if-then" rule triggers for automatic execution of cross-protocol operations, and the architecture is divided into three layers: Planning (task and trigger definitions), 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 deployment stage.

AFI Protocol

AFI Protocol is an algorithm-driven agent execution network that supports 24/7 unmanaged automated operations, focusing on solving the issues of execution decentralization, strategy thresholds, and risk response in Decentralized Finance. Its design is aimed at institutions and advanced users, providing orchestratable strategies, permission management, and SDK tools, and launching the yield-bearing stablecoin afiUSD.