The Evolution of DeFi Intelligence: From Automation Tools to AgentFi

In the current cryptocurrency industry, stablecoin payments and Decentralized Finance applications are among the few tracks that have been verified to possess real demand and long-term value. At the same time, the flourishing Agents are gradually becoming the practical manifestation of user interfaces in the AI industry, serving as the 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 are mainly focused on three typical scenarios:

1. Conversational Interactive Agents: Primarily focused on chatting, 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 to enter the market and capture 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 obvious standout projects yet.

3. Strategy Execution Agent: Centered 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, with the potential to break through speculative bottlenecks and form a smart execution infrastructure that possesses 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 intelligent DeFi, we can divide the system capabilities into three stages: Automation( automated tools), Intent-Centric Copilot( intent-driven assistant), and AgentFi( on-chain intelligent agents).

• Automation is more like a rule trigger ( Rule Trigger ): It executes fixed tasks based on preset conditions, such as arbitrage, rebalancing, profit-taking, and stop-loss, and cannot generate strategies or operate independently.

• Copilot introduces intent recognition and semantic parsing capabilities, allowing users to input natural language, with the system understanding, decomposing, and suggesting execution paths, but ultimately still requiring user confirmation, resulting in an open execution chain.

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

| 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, 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 decomposition | No human interaction needed, can collaborate with human/Agent | | Intelligence Level | Low, Process Automation | Medium, Interactive Understanding | High, Autonomous Strategy Generation and Evolution | | Strategy Capability | None, performs preset tasks | Limited, relies on user instructions | Strong, can self-learn and optimize combinations | | Implementation Difficulty | Low, focused on backend services | Medium, requires strong frontend interaction design | High, requires deep collaboration with 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 ( is not a static input, but rather real-time monitoring ).
2. Has the ability to generate and combine strategies. ( is not a preset strategy, but can self-develop an action plan based on context. )
3. Can autonomously execute operations on the chain ( without user interaction, able to perform complex operations such as swap/lend/stake ).
4. With persistent state and evolutionary capability, ( Agent has a lifecycle, can run long-term, 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 more ≠ AgentFi: automated trading is merely a "rule trigger", Copilot can understand user intentions and provide operational suggestions, but still relies on human involvement; whereas true AgentFi is an "intelligent entity with perception, reasoning, and on-chain autonomous execution capabilities", able to complete strategy loops and continuous evolution 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-type finance. 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 characteristic is "intention-driven + single atomic interaction". The trading process does not involve profit strategies, state maintenance, or evolution logic, and is mostly applicable to 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 in the market are currently at this stage, which do 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 leveraged rebalancing scenarios, ) actually require the capability of AI Agents to be integrated, which is still in the early exploration stage.

| Scenario Category | Continuous Income | AgentFi Compatibility | Implementation Difficulty | Description | |----------------|------------|-------------------------------|------------|----------------------------------------------------| | Swap Transaction | ❌ No | ⚠️ Partially compatible ( only Intent transactions are not true AgentFi ) | ✅ Easy to implement | Single atomic operation ( such as token swap ), 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 adjustment, with very low AI participation. | 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 | ⚠️ Partial Compatibility | ✅ Moderate | Primarily based on automation tools; if it can combine multiple platform quotes or maximize profit paths, it can be executed by lightweight Agents, but it is difficult to evolve into a long-term intelligent agent | | ✅Swap Trading Combination | ✅Potential for Profits | ✅Immature | ❌Difficult to Achieve | Strategies such as cross-asset arbitrage, perpetual hedge LP, dynamic position adjustment, etc., require complex strategy engines for support, and currently remain 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 target ( APR / APY ) facilitates Agent to establish optimization functions;
• The strategy portfolio space is broad, 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 (Agent).

| Rank | Scenario Category | Continuous Income | AgentFi Compatibility | Engineering Difficulty | Description | |--------|------------------------------------|------------|-----------------|----------|---------------------------------------------| | 1 | Liquidity Mining | ✅Yes | ✅✅✅Very High | ❌High | Strategies need 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 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; agents can optimize trading timing and yield stability | | 4 | Funding Rate Arbitrage ( Perp/CeFi/Decentralized Finance Hybrid ) | ✅ Yes | ✅✅ High | ❌ Very High | Multi-market arbitrage has AI advantages, but the complexity of off-chain interactions and collaborations 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 Agent, but dynamic combinations like multiple LST + Lending + LP can be involved. | 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 |

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

High Priority Business Landing Direction:

• 借贷(Lending / Borrowing): The 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 are highly volatile. AgentFi can significantly optimize annualized returns and interaction efficiency, but the engineering implementation has 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 interaction need to be addressed, making it technically difficult;
• 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, Agent can provide assistance in portfolio optimization and maturity strategy;

Introduction to 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 provided conditional trigger task execution support for protocols like Aave and Reflexer. However, 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 and Instadapp ). These platforms integrate standardized automated execution modules, including Limit Order settings, liquidation protection, automatic rebalancing, DCA, and grid strategies. 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 parties, supporting the creation of programmable automation tasks on chains such as Arbitrum, Base, and Optimism. Its core functionality is achieved through "if-then" rule triggers to automatically execute cross-protocol operations. The architecture is divided into Planning( task and trigger definition), Execution( intent broadcasting and execution bidding), and Security( triple verification and security control) three layers. Currently, it adopts SDK integration, 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 issues of execution decentralization, strategy thresholds, and risk response in Decentralized Finance.