AI Agent protocol stack and DeFi practice: In-depth analysis of Theoriq

Since 2024, AI Agents have rapidly emerged in the Web3 world, with a large number of experiments surrounding agents taking place. From the perspective of the full AI chain, AI Agents not only serve the role of "interaction and execution layer" but also represent a user-oriented intelligent product form. As an intermediary bridge between model capabilities and specific business applications, AI Agents encapsulate underlying AI models into intelligent agents with task-oriented and autonomous capabilities, directly serving users to autonomously execute tasks and generate real economic activities.

1. AI Agent protocol stack level

In the entire AI Agent protocol stack, we can divide it into three main levels:

Infrastructure Layer

This layer provides the fundamental operational support for agents and serves as the technical foundation for all Agent systems.

• Core Module: includes Agent Framework and Agent OS, providing core capabilities for the lifecycle management of Agents.
• Supported modules: such as Agent DID, Agent Wallet & Abstraction, Agent Payment/Settlement.

Coordination and Scheduling Layer

Focusing on the collaboration, task scheduling, and system incentive mechanisms among multiple agents is key to building the "collective intelligence" of agent systems.

• Agent Orchestration: Unified scheduling and management of Agent lifecycle, task allocation, and execution processes.
• Agent Swarm: emphasizes the collaboration of distributed intelligent agents, possessing high autonomy, division of labor capabilities, and resilient cooperation.
• Agent Incentive Layer: Build the economic incentive system for the Agent network.

Application Layer

• Distribution Subclasses: including Agent Launchpad, Agent Marketplace, and Agent Plugin Network, etc.
• Application subcategories: including AgentFi, Agent Native DApp, Agent-as-a-Service, etc.
• Consumer Subcategory: Mainly Agent Social / Consumer Agent, aimed at lightweight scenarios such as consumers / entertainment / social.
• Meme: Speculating on the Agent concept, usually lacking actual technical implementation and application landing.

2. AgentFi: A Practical and Value-Proven Implementation Direction

At the beginning of 2024, Autonolas and Morpheus, among other platform-based Agent projects focusing on "coordinated scheduling + incentive mechanisms", were the first to gain attention in the market. However, by the end of 2024, it was the Agent Launchpad projects represented by Virtual Protocol, along with numerous token projects for AI Agent Memes, that truly ignited the industry's enthusiasm.

The explosive growth of Agent Launchpad is essentially benefited by the speculative sentiment triggered by Meme coins, characterized by strong narratives, high dissemination, low engineering thresholds, and ease of replication. However, due to the lack of mid- to long-term value loops and real application support, such projects often easily fall into the dilemma of narrative exhaustion and user loss. In contrast, the long-term value of platform-type or framework-type Agent projects depends on whether they can build a "closed-loop intelligent collaborative network" around real business scenarios—such as implementing multi-Agent collaboration models and deeply binding with real economic activities.

Stablecoins, payments, and DeFi are among the few sectors in the current crypto industry that have been validated to possess real demand and long-term value. In the practical application direction of AI Agents, the most feasible and user-value driven areas in the short term are primarily concentrated in two fields: the first is interactive Agents in the Chat/Social/Assistant category. Although some are shell applications, they have a low interaction threshold and can quickly connect to the Web2 user experience; the second is strategy execution Agents aimed at DeFi, where on-chain fund operations provide direct economic returns, with short feedback cycles and quantifiable strategy effects.

As the industry gradually shifts from narrative to practicality, AI teams with engineering implementation capabilities and the ability to deliver real value will become key drivers. We believe that AgentFi may be the most promising evolutionary direction at this stage to achieve a balance between "engineering feasibility + business usability."

In terms of the current on-chain asset structure and the automation level of Agents, the actual implementation direction of AgentFi mainly focuses on the following several sectors:

1. Lending Type: Focused on automated interest rate arbitrage and cross-protocol capital allocation, helping users optimize lending and borrowing returns across multiple lending markets; future expectations include support for risk exposure management and leverage strategy execution.

2. Trading Category: Focus more on "intent-based automated execution" rather than "smart profit-making"; the product logic leans towards "trading assistant Copilot" instead of "AI that makes money for you in trading". It is expected that future scenarios will include automated execution in cross-platform arbitrage, trend following, grid trading, and reversal strategies.

3. Liquidity Mining/LP Management: Focused on automated execution of centralized LP strategies like Uniswap V3, smart rebalancing under volatility awareness, and optimal allocation of incentivized funds in protocols such as Curve and Balancer. This direction presents the greatest technical challenges, but also has the strongest long-term potential and innovative space.

From the perspectives of strategy complexity, real-time requirements, infrastructure dependence, and yield determinacy, lending-type Agents have a high feasibility for implementation due to their stable data structure and relatively simple strategies. In contrast, LP management is one of the directions with the highest technical barriers in the AgentFi field. Such strategies need to respond in real-time to price fluctuations, volatility changes, and fee accumulation situations, which imposes extremely high demands on the Agent's data perception, strategy judgment, and on-chain execution. Compared to lending and trading Agents, LP Agents not only need to accurately predict market conditions but also require dynamic rebalancing and yield redistribution operations on-chain, which significantly increases engineering complexity.

3. Theoriq: Evolution of Liquidity Management for Agent Swarm

Theoriq aims to create an intelligent agent economy through the coordination of AI agent clusters, and on-chain liquidity management and yield optimization is one of its important application scenarios. The flagship product Alphaswarm of Theoriq has chosen liquidity management, which has the highest technical threshold in AgentFi, as its entry point, in sharp contrast to narrative-driven, low-threshold Launchpad or Meme projects.

Theoriq Alpha Protocol and AlphaSwarm officially launch on the mainnet journey.

On May 29, 2025, Theoriq officially released its roadmap for the mainnet launch, renaming Theoriq Protocol to Theoriq Alpha, while also announcing the first flagship application AlphaSwarm on Theoriq Alpha, aiming to better reflect its positioning in the "Agentic Economy" and the core of multi-agent collaboration, with the mainnet set to launch in July 2025.

Theoriq Alpha Protocol: A communication and execution protocol stack designed for Agent collaboration

Theoriq Alpha Protocol is a decentralized protocol launched by Theoriq, designed to support multi-agent collaboration on-chain, execute complex financial tasks, and optimize liquidity. It is not only a framework for agent operation but also a complete system with high communication capabilities and a modular infrastructure, aimed at filling the current infrastructure gaps in DeFi regarding agent coordination and execution. Its core features include:

• Messaging and Coordination Mechanism: Supports communication between agents with secure authentication and between users and agents, compatible with synchronous calls and asynchronous messages; built-in Pub/Sub stream, supports agents to initiate transaction proposals in real-time and collaboratively execute tasks.
• Agent Public Registration System: All Agents will have a permanent on-chain ID and off-chain metadata for easy discovery and integration; initially deployed on the Base mainnet, supporting free registration and combination of Agents within the ecosystem.
• Configurable Agent Templates: Provide no-code parameter settings, support customized strategy logic; project parties and token holder communities can flexibly configure Agent behaviors to achieve personalized automated execution.
• Developer Access Tools: Provides a stateless REST API and official Python SDK; lowers integration barriers, making it easier for developers to quickly build and deploy compatible Agents.
• AlphaStudio User Terminal: Provides a one-stop Agent management and interaction platform; users can browse, invoke, and combine various registered Agents, which is the core entry point into AgentFi.

The Theoriq team has established partnerships with multiple Decentralized Finance protocols to promote the deep integration of Agent's native collaborative capabilities with on-chain liquidity management. Theoriq is building the "Agent Operating System" in the DeFi world through Alpha Protocol, while AlphaSwarm is its first practical collaborative intelligent agent system, gradually moving from information extraction and strategy generation to automated capital management and strategy execution.

Theoriq AlphaSwarm: The first tangible Swarm Agent system

AlphaSwarm is the first flagship multi-agent system built on the Theoriq Alpha Protocol, demonstrating the full-process intelligent collaborative capability from strategy generation to on-chain execution. The current version consists of three types of core agents:

• AlphaSwarm Portal Agent: Perceives user wallet status, acts as an entry coordinator;
• AlphaSwarm Knowledge Agent: Access on-chain/off-chain data to generate precise insights and strategic judgments;
• AlphaSwarm LP Assistant Agent: Generates on-chain trading proposals based on set parameters to achieve automated liquidity management.

The three together constitute a task discovery ( Portal Agent )--- Data Analysis ( Knowledge Agent )--- Strategy Generation ( LP Agent )--- On-Chain Execution ( User Signature )'s complete closed-loop process, allowing users to complete asset allocation and dynamic adjustments without manual operation. In the future, AlphaSwarm will not be limited to LP management, with plans to expand to yield aggregation and reinvestment, Staking/Restaking intelligent scheduling, cross-chain portfolio strategy management, and on-chain fund flow intelligent scheduling. Its goal is to create an Agent Swarm with "superhuman-level" understanding and execution capabilities to provide users with an end-to-end on-chain asset management assistant.

Four, Theoriq Ecological Cooperation and Community Development

Theoriq is building a multidimensional ecological network encompassing AI infrastructure, data collaboration, computing power acceleration, and community co-construction, and has reached in-depth cooperation with several top technology companies and Web3 infrastructure projects. With the support of the Google Cloud AI Startup Program and NVIDIA Inception Program, Theoriq has obtained hundreds of thousands of dollars in cloud resources and high-performance GPU support, significantly enhancing the training efficiency and execution capability of AI Agents.

At the data and computing level, Theoriq is collaborating with multiple key infrastructure partners to build a modular capability network:

• Kaito: Important community ecological partner, supports leaderboard incentive mechanism and Mindshare content dissemination;
• Arrakis Finance and Keyrock collaborate as partners for treasury management and market-making strategies.
• Aethir and Hyperbolic: provide decentralized high-performance computing resources, supporting inference under a multi-agent architecture;
• The Graph: Ensure that agents have agile and precise market perception in the execution of DeFi strategies;
• Cookie.fun: Provides user intent and behavior data support;

These collaborations have formed an ecological closed loop covering multiple dimensions of data, computing power, strategies, and community, providing solid support for Theoriq's AgentFi network.

At the community level, Theoriq has launched the "Infinity Swarm" Global Ambassador Program, establishing multiple tiers for content creators and community builders, offering early access, USDC rewards, offline event tickets, and limited-edition merchandise as incentives. By collaborating with Kaito to launch the Yapper Leaderboard, Theoriq rapidly gathered hundreds of thousands of active members in a short period, significantly enhancing community engagement and outreach. The team also actively participates in global core crypto conferences such as ETHDenver, DevCon, and SmartCon, and has hosted or co-hosted Hackathons multiple times, continuously expanding its technical influence and ecological appeal in the AgentFi field.

Theoriq Alpha has constructed an ecological flywheel mechanism driven by four core participants, with each party's involvement enhancing the overall system value, creating a positive feedback loop:

• AI Developers: including Agent builders, AI frameworks, data providers, and AI Infra teams, can access data, coordinate execution, and develop new strategies through AlphaSwarm, and earn income from trade execution and profit sharing, realizing the implementation and distribution of strategies.
• DeFi protocols: Such as DEX, yield protocols, market makers, Intent protocols, and Vaults, can achieve automated operations by integrating with Agents, enhancing liquidity Depth and trading volume, and promoting protocol activity and capital efficiency improvements.
• Token project parties: Project parties with capital reserves and community foundations can leverage AlphaSwarm to optimize capital utilization and community participation through smart