A New Era of Smart Trading: The Fusion of Intent Trading and AI Agents

Introduction

One of the future development directions of blockchain applications is intent-based trading. This article explores the concept of intent-based trading and its potential, analyzing how this model simplifies user experience, enhances transaction security, and brings innovative opportunities for decentralized applications. We also discuss the role of artificial intelligence agents, exploring how they can integrate with intent-based trading to drive the automation and intelligence of smart contracts, providing users with a more intelligent and personalized blockchain interaction experience.

Intent Trading Overview

Intent-based trading refers to a user-centric approach to executing blockchain operations with specific goals. Users only need to express their final objectives, such as time, price, and other transaction conditions, and do not need to worry about the specific steps involved. Users sign a contract and "outsource" the trade to a third party. The intermediary steps are handled by third-party problem solvers, which may be individuals or programs. As long as the output falls within the scope defined by the user's intent, the solver can freely achieve the results. Users typically need to pay a fee to the solver to complete the transaction.

( Core features of intent trading

1. Use a declarative programming approach to directly declare the expected outcome of the transaction, rather than specifying the exact steps.

2. After the user defines the trading intent, the actual transaction construction is completed by a third-party resolver.

Intentional trading applies to virtual currencies that have the same identity, such as Bitcoin. This characteristic ensures consistency and interchangeability in transactions.

Advantages and Applications of Intent Trading

Intent trading simplifies the trading process and enhances user experience. It supports repeat trading, time-related or condition-based trading, such as automatic balance top-ups. This approach helps newcomers use cryptocurrencies without having to deal with complicated steps.

Intentional trading can also reduce slippage, as the system can execute orders at the most favorable times in the market. The solver will seek the best path and may sometimes aggregate orders for larger trades to further reduce slippage.

Potential applications include: setting limit orders, regularly purchasing tokens, automatically transferring funds when the balance is insufficient, and timely buying and selling tokens based on significant events reported by oracles.

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Limitations of Traditional Trading Models

The current trading model suffers from opacity and centralization risks. Users have limited understanding of the transaction execution process and are susceptible to MEV attacks such as front-running and back-running. The freedom of transactions possessed by miners, validators, and relayers allows them to easily extract value.

To prevent sandwich attacks and other MEV attacks, some platforms have introduced new order types, such as "flash bot trading." Randomized trading strategies can also increase the difficulty of market manipulation.

UniswapX: Practical Cases of Intentional Trading

UniswapX is an innovative decentralized trading protocol that employs a permissionless, open-source auction mechanism. It allows users to trade across different AMMs and liquidity sources, with the core being intent-based trading.

UniswapX includes three types of reactors: limit orders, Dutch auctions, and exclusive Dutch auction reactors. When users place an order, they enter into a contract with Permit2, allowing token transfers. After the order is published, anyone can take the order to complete it.

UniswapX provides users with an efficient, transparent, and user-friendly trading environment through its innovative mechanism, addressing issues faced by traditional AMMs, such as trading costs, MEV attacks, and slippage.

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Artificial Intelligence Agent ( AI-Agent ) Overview

AI-Agent is a computer program that can autonomously make decisions and execute tasks based on the environment, input, and predefined goals. Its core components include:

• Large language model ( LLM ) as the "brain"
• Observation and Perception Mechanism
• Reasoning process
• Action Execution
• Memory and Retrieval

AI-Agent can be reactive, proactive, learning, or collaborative. Common examples include ChatGPT, Tesla's Autopilot engine, and Netflix's recommendation engine.

AI agents are widely used across various fields, including e-commerce, education, real estate, tourism, finance, healthcare, and transportation. They provide services such as personalized recommendations, intelligent customer service, and market analysis, offering advantages such as automated task execution, personalized experiences, improved usability, and cost savings.

The Combination of AI-Agent and Intent Trading

In intent trading, the AI-Agent can serve as an intelligent personal assistant, understanding user natural language input and completing tasks. LLM can be integrated into the intent framework, allowing users to express their needs without worrying about implementation details.

The AI-Agent can interact with the solver, automatically execute trading strategies, and optimize the price and timing of trade execution. It can quickly interpret user intentions, communicate with the solver, and generate results. The solver can connect to multiple liquidity sources to find the optimal trading exchange rate and reduce cross-chain transaction gas fees.

Future Outlook

The TXT2TXN prototype from Circle demonstrates the potential of combining AI-Agent with intent trading. Users only need to input their intent, and the LLM can identify and execute the corresponding actions. Possible future improvements include:

• AI poses questions to ensure correct understanding of intent
• Extended functionalities, such as purchasing NFTs or tokens through dApps
• Integrate personal address book to enhance user experience

However, we also need to pay attention to the problems that solvers may bring, such as information leakage and market manipulation risks. Balancing efficiency and security will be an important topic for future development.