This article focuses on the convergence of DeFi and AI, outlining its developmental stages from automation to intelligence, and analyzing the infrastructure, application scenarios, and key challenges of strategy-executing Agents.

In the current crypto industry, stablecoin payments and DeFi applications are among the few sectors proven to have genuine demand and long-term value. Meanwhile, the flourishing field of Agents has gradually become the practical user-facing implementation of AI, serving as a critical intermediary layer connecting AI capabilities with user needs.

In the intersection of Crypto and AI—particularly in the direction of AI enhancing Crypto applications—current explorations primarily focus on three key scenarios:

1. Conversational Agents: Centered on chatbots, companionship, and assistants. While many remain wrappers around general-purpose large models, their low development barriers, natural interaction, and token incentives have made them the earliest form to capture user attention.

2. Information-Integration Agents: Focused on intelligently aggregating online and on-chain data. Projects like Kaito and AIXBT have succeeded in online (but off-chain) information search and integration, while on-chain data aggregation remains exploratory with no clear frontrunners yet.

3. Strategy-Execution Agents: Extending from stablecoin payments and DeFi strategy execution into Agent Payment and DeFAI. These Agents are deeply embedded in on-chain transactions and asset management logic, potentially breaking through speculative hype to form intelligent execution infrastructure with financial efficiency and sustainable yields.

This article will focus on the evolutionary path of DeFi and AI convergence, categorizing its stages from automation to intelligence, and analyzing the infrastructure, scenarios, and challenges of strategy-executing Agents.

The evolution of DeFi intelligence can be divided into three stages: Automation, Intent-Centric Copilot, and AgentFi.

• Automation acts as a rule trigger—executing fixed tasks (e.g., arbitrage, rebalancing, stop-loss) based on preset conditions. It cannot generate strategies or operate independently.

• Copilot introduces intent recognition and semantic parsing. Users input natural language, and the system suggests execution paths, but final confirmation remains manual, leaving the execution chain incomplete.

• AgentFi represents a full "perception → reasoning/strategy generation → on-chain execution → evolution" loop. These are autonomous, self-evolving Agents capable of on-chain governance.

Key Criteria for AgentFi:
To qualify as AgentFi, a project must meet at least three of these five standards:

1. Autonomous perception of on-chain/market signals (real-time monitoring, not static input).

2. Strategy generation and composition (not preset strategies, but context-aware planning).

3. Autonomous on-chain execution (no user input required for swaps/lending/staking).

4. Persistent state and evolution (long-term operation with feedback-driven adjustments).

5. Agent-native architecture (e.g., dedicated SDKs, execution environments, middleware).

In short: Automation ≠ Copilot ≠ AgentFi. Automation is a "rule trigger," Copilot assists but relies on human input, while AgentFi is a fully autonomous, self-optimizing on-chain entity.

DeFi applications broadly fall into two categories with differing suitability for intelligence:

Includes swaps, bridges, and fiat on/off-ramps—characterized by intent-driven, atomic interactions with no ongoing yield strategies or state maintenance. These fit Intent-Centric Copilots, not AgentFi.

These involve quantifiable targets (APR/APY), diverse strategy combinations, and dynamic management—making them ideal for AgentFi.

Priority for AgentFi Adoption:

• High: Lending (standardized logic) & Liquidity Mining (high yield complexity).

• Medium/Long-Term: Pendle, Funding Arbitrage, LRT Strategies.

• Low: RWA (due to compliance barriers).

• Mimic.fi: On-chain automation platform for developers (Arbitrum, Base, Optimism).

• AFI Protocol: Algorithmic Agent network for institutional DeFi (in beta).

• HeyElsa: Multichain DeFi assistant (10+ chains, $1M daily volume).

• Bankr: Social-integrated intent executor (Base/Solana/Polygon/ETH).

• Griffain: Solana-focused multi-Agent platform.

• Giza ARMA: Stablecoin yield optimizer (Aave, Compound, Morpho).

• Theoriq AlphaSwarm: Multi-Agent liquidity management OS.

• Almanak: Python-based strategy engine for DeFi automation.

• Brahma: Smart account orchestration layer (feUSD, Morpho integrations).

• Olas Network (BabyDegen): Multi-chain Agents for trading/portfolio management.

• Axal: Autopilot Yield for conservative/aggressive strategies (Aave, Pendle, Kamino).

AgentFi represents the next leap in DeFi intelligence, moving beyond automation and Copilots to fully autonomous, self-optimizing systems. While challenges remain—particularly in cross-protocol execution and compliance—projects like Giza ARMA and Theoriq are paving the way for a new era of on-chain financial efficiency.

This article focuses on the convergence of DeFi and AI, outlining its developmental stages from automation to intelligence, and analyzing the infrastructure, application scenarios, and key challenges of strategy-executing Agents.

In the current crypto industry, stablecoin payments and DeFi applications are among the few sectors proven to have genuine demand and long-term value. Meanwhile, the flourishing field of Agents has gradually become the practical user-facing implementation of AI, serving as a critical intermediary layer connecting AI capabilities with user needs.

In the intersection of Crypto and AI—particularly in the direction of AI enhancing Crypto applications—current explorations primarily focus on three key scenarios:

1. Conversational Agents: Centered on chatbots, companionship, and assistants. While many remain wrappers around general-purpose large models, their low development barriers, natural interaction, and token incentives have made them the earliest form to capture user attention.

2. Information-Integration Agents: Focused on intelligently aggregating online and on-chain data. Projects like Kaito and AIXBT have succeeded in online (but off-chain) information search and integration, while on-chain data aggregation remains exploratory with no clear frontrunners yet.

3. Strategy-Execution Agents: Extending from stablecoin payments and DeFi strategy execution into Agent Payment and DeFAI. These Agents are deeply embedded in on-chain transactions and asset management logic, potentially breaking through speculative hype to form intelligent execution infrastructure with financial efficiency and sustainable yields.

This article will focus on the evolutionary path of DeFi and AI convergence, categorizing its stages from automation to intelligence, and analyzing the infrastructure, scenarios, and challenges of strategy-executing Agents.

The evolution of DeFi intelligence can be divided into three stages: Automation, Intent-Centric Copilot, and AgentFi.

• Automation acts as a rule trigger—executing fixed tasks (e.g., arbitrage, rebalancing, stop-loss) based on preset conditions. It cannot generate strategies or operate independently.

• Copilot introduces intent recognition and semantic parsing. Users input natural language, and the system suggests execution paths, but final confirmation remains manual, leaving the execution chain incomplete.

• AgentFi represents a full "perception → reasoning/strategy generation → on-chain execution → evolution" loop. These are autonomous, self-evolving Agents capable of on-chain governance.

Key Criteria for AgentFi:
To qualify as AgentFi, a project must meet at least three of these five standards:

1. Autonomous perception of on-chain/market signals (real-time monitoring, not static input).

2. Strategy generation and composition (not preset strategies, but context-aware planning).

3. Autonomous on-chain execution (no user input required for swaps/lending/staking).

4. Persistent state and evolution (long-term operation with feedback-driven adjustments).

5. Agent-native architecture (e.g., dedicated SDKs, execution environments, middleware).

In short: Automation ≠ Copilot ≠ AgentFi. Automation is a "rule trigger," Copilot assists but relies on human input, while AgentFi is a fully autonomous, self-optimizing on-chain entity.

DeFi applications broadly fall into two categories with differing suitability for intelligence:

Includes swaps, bridges, and fiat on/off-ramps—characterized by intent-driven, atomic interactions with no ongoing yield strategies or state maintenance. These fit Intent-Centric Copilots, not AgentFi.

These involve quantifiable targets (APR/APY), diverse strategy combinations, and dynamic management—making them ideal for AgentFi.

Priority for AgentFi Adoption:

• High: Lending (standardized logic) & Liquidity Mining (high yield complexity).

• Medium/Long-Term: Pendle, Funding Arbitrage, LRT Strategies.

• Low: RWA (due to compliance barriers).

• Mimic.fi: On-chain automation platform for developers (Arbitrum, Base, Optimism).

• AFI Protocol: Algorithmic Agent network for institutional DeFi (in beta).

• HeyElsa: Multichain DeFi assistant (10+ chains, $1M daily volume).

• Bankr: Social-integrated intent executor (Base/Solana/Polygon/ETH).

• Griffain: Solana-focused multi-Agent platform.

• Giza ARMA: Stablecoin yield optimizer (Aave, Compound, Morpho).

• Theoriq AlphaSwarm: Multi-Agent liquidity management OS.

• Almanak: Python-based strategy engine for DeFi automation.

• Brahma: Smart account orchestration layer (feUSD, Morpho integrations).

• Olas Network (BabyDegen): Multi-chain Agents for trading/portfolio management.

• Axal: Autopilot Yield for conservative/aggressive strategies (Aave, Pendle, Kamino).

AgentFi represents the next leap in DeFi intelligence, moving beyond automation and Copilots to fully autonomous, self-optimizing systems. While challenges remain—particularly in cross-protocol execution and compliance—projects like Giza ARMA and Theoriq are paving the way for a new era of on-chain financial efficiency.