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 tracks that have been proven to have real demand and long-term value. At the same time, the flourishing Agents are gradually becoming the actual implementation form of user interfaces in the AI industry, serving as a key intermediary 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 explorations are mainly focused on three typical scenarios:

1. Conversational Interactive Agents: Mainly focused on chat, companionship, and assistant roles. Although most are still wrappers of general large models, their low development threshold and natural interactions, combined with token incentives, have made them one of the earliest forms to be 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 the direction of on-chain data integration is still in the exploratory stage with no significant standout projects.

3. Strategy Execution Agent: Based on stablecoin payments and the execution of DeFi strategies, it extends into two main directions: Agent Payment and DeFAI. This type of Agent is 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, 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 intelligent DeFi, we can divide system capabilities into three stages: Automation( automated tools), Intent-Centric Copilot( intention-driven assistant), and AgentFi( on-chain intelligent agents).

• Automation is more like a rule trigger ( Rule Trigger ): 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 analysis capabilities. Users input natural language, and 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 closed loop, which is an intelligent entity with on-chain autonomous execution and continuous evolution capabilities (Agent).

|Dimension|Automated Infra|Intent-Centric Copilot|AgentFi| |---|---|---|---| |Core Logic|Rule Trigger + Condition Execution|Intent Recognition + Action Guidance|Strategy Feedback Loop + Autonomous Execution| |Execution Method|Triggered 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 triggers executed|Users express intentions through prompts, system assists in breakdown|No human interaction needed, can collaborate with people/agents| |Intelligence Level|Low, Process Automation|Medium, Interactive Understanding|High, Autonomous Strategy Generation and Evolution| |Strategy Ability|None, Executes preset tasks|Limited, Depends on user commands|Strong, Can self-learn and optimize combinations| |Implementation Difficulty|Low, focusing on backend services|Medium, requires strong frontend interaction design|High, requires deep collaboration between 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 ( is not a static input, but real-time monitoring ).
2. Possessing the ability to generate and combine strategies ( is not a preset strategy, but rather the ability to autonomously formulate action plans based on context ).
3. Can autonomously execute operations on the chain ( without user interaction, capable of executing complex operations such as swap/lend/stake ).
4. With persistent state and evolutionary capability ( Agent has a lifecycle, can run for a long time and self-adjust based on feedback )
5. Equipped with Agent-Native architecture ( such as exclusive Agent SDK, hosted execution environment, Agent middleware, etc. )

In other words, automated trading ≠ Copilot, and even more so ≠ AgentFi: automated trading is merely a "rule trigger", while Copilot can understand user intentions and provide operational suggestions, it still relies on human involvement; true AgentFi is an "intelligent agent with perception, reasoning, and on-chain autonomous execution capabilities", capable of completing strategy loops and continuous evolution without human intervention.

Decentralized Finance Scenario Smart Adaptability Analysis

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

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 deposit and withdrawal. Their essential characteristic is "intention-driven + single atomic interaction". The trading process does not involve yield strategies, state maintenance, or evolution logic, and is mostly suitable for the lightweight execution path of Intent-Centric Copilot, and does not belong to AgentFi.

Due to its low engineering threshold and simple interaction, most DeFi projects in the market are currently at this stage, which does not constitute a closed-loop intelligent agent for AgentFi; however, for a few advanced complex Swap strategies ( such as cross-asset arbitrage, perpetual hedging LP, and leverage rebalancing, the ability of AI Agent needs to be integrated, and it is still in the early exploration stage.

|Scenario Category|Sustainable Returns|AgentFi Compatibility|Implementation Difficulty|Description| |---|---|---|---|---| |Swap Trading|❌ No|⚠️ Partially compatible ) only Intent trading is not true AgentFi (|✅ Easy to implement|Single atomic operation ) like swapping coins (, no strategy state accumulation, suitable for Copilot invocation.| |Cross-chain Bridge|❌ No|❌ Weak|✅ Easy to Implement|Cross-chain is intermediary transmission, does not involve strategy planning and adjustment, AI participation is very low.| |Fiat Deposit and Withdrawal|❌ No|❌ None|❌ Uncontrollable|Highly dependent on CeFi channels and compliance processes, on-chain Agents cannot autonomously initiate operations| |Aggregation Optimization|⚠️ Not Guaranteed|⚠️ Partially Adapted|✅ Moderate|Primarily based on automated tools. If it can combine multiple platform quotes or maximize profit paths, it can be executed by a lightweight agent, but it's difficult to evolve into a long-term intelligent agent| |✅ Swap trading combinations|✅ Potential for profit|✅ Immature|❌ Difficult to implement|Options such as cross-asset arbitrage, perpetual hedge LP, dynamic position allocation, 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 return targets ### APR / APY ( facilitate Agent in establishing optimization functions;
• The strategy portfolio space is vast, covering multiple assets, multiple time periods, multiple platforms, and multiple interaction processes;
• Operations require frequent management and real-time adjustments, suitable for execution and maintenance by on-chain intelligent agents )Agent(.

|Rank|Scenario Category|Sustainable 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 adjustment easily achievable| |3|Pendle)PT/YT yield rights trading(|✅ Yes|✅✅ High|❌ High|The yield period and structure are diverse, and the combination trading is complex, allowing the intelligent agent to optimize the timing of buying and selling as well as the stability of returns| |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 interaction and collaboration is extremely high, still in the exploratory 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 intervened by intelligent agents| |6|RWA) Real World Assets (|⚠️ Stable Returns|❌ Low|⚠️ High Compliance|Stable return structure, high compliance threshold, no interoperability between protocols, short-term lack of space for AgentFi strategy implementation|

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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 smart agents.
• Liquidity Mining ( Yield Farming ): The pool dynamics are frequent, the strategy combination space is large, and the returns fluctuate greatly. 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 Agents to manage expiration rotation and inter-pool arbitrage.
• Funding Rate Arbitrage: Theoretical returns are considerable, but challenges in cross-market execution and off-chain interaction need to be addressed, making it a complex engineering task.
• 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 Intelligent Projects in Decentralized Finance 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 such as Aave and Reflexer. However, 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 and 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 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 automated cross-protocol operations, with the architecture divided into Planning) tasks and trigger definitions###, Execution( intent broadcasting and execution bidding), and Security( triple verification and security control) layers. Currently, it adopts an SDK integration approach, 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 automation 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 orchestrated strategies, permission management, and SDK tools, and has launched the yield-bearing stablecoin afiUSD as its native asset. It is currently in the Sonic Labs internal testing phase and has not yet been publicly launched or made available for retail users.

) 2. Intent-Centric Copilot ###: Intent Expression and Execution Suggestions

The DeFi that was once popular at the end of 2024.