Agent-Fi on AO : Fusion of AI agent financial paradigm

Imagine in the future world, AI agent intelligent agents will form a digital symbiotic relationship with humans. Autonomous agents can clearly understand the intention in the conversation based on natural language requests from users, automatically break down tasks, and achieve expected results.

AO has established an asynchronous parallel network based on the Actor, which does not achieve consensus on the entire contract calculation process, but achieves consensus only on the transaction sequence. It is optimistically assumed that the fixed transaction order will result in consistent execution in the virtual machine. This choice allows the computation of the AO network to be massively scaled up until it supports arbitrary types of computation. The AR network serves as the consensus layer for transaction sequence and the storage layer for transaction result states.

Compared with most other mainstream blockchain projects, which are mostly standalone blockchains that only support native state machine smart contracts from the underlying level, AO’s infrastructure compatibility can support more complex computing capabilities, including the operation of AI models.

After the recent update of the WASM virtual machine, AO’s computing unit (Compute Unit) can now access 16GB of memory, which means we can download and execute 16GB models on AO. 16GB is enough to run large language model calculations, such as the unquantized version of Llama 3 Falcon series and many other models.

At the same time, AO uses WeaveDrive to allow users to access Arweave data within AO as if accessing a local hard drive, and it is compatible with highly heterogeneous processes of different types of virtual machines to interact in a shared environment, which means that we have more data sources and combination possibilities. This also means that in the future, when building applications, users have more motivation to upload data to Arweave because this data can also be used in AO programs. The AO development team has uploaded model data worth about $1000 to the network when testing large language models running in the AO+AR system, but this is just the beginning.

AO’s system design makes it possible to implement smart contracts that incorporate AI agents. By programming in AO, we create AI agents to make intelligent decisions in the marketplace, and agents may be against each other or on behalf of humans against humans. “When we look at the global financial system, about 83% of transactions on the Nasdaq are executed by bots.” Today’s quantitative trading is the predecessor of AI proxy trading, but in the future, the process of designing and selecting machine learning models to execute automated transactions will be easier for AI to “unbox” and automate.

In the past few years, the development of DeFi has made it possible to execute various financial operations on-chain without the need to trust centralized entities, such as borrowing, token trading, or derivatives. However, when we talk about the market, it is not just the reliability of these operations that matters. In fact, reliable execution of various operations is only the foundation. The core factor that determines whether a market is vibrant is still the flow of capital, the people who decide to buy, sell, borrow, or participate in various financial games. Currently, if you want to participate in cryptocurrency investment without doing all the research and participation yourself, you must find a reliable fund, trust them to manage your funds, and delegate authority to fund members to make intelligent decisions. But with the development of AI applications, we may be able to expand the intelligent decision-making part of the market, filter information in the network, process data, combine strategies, and integrate the wisdom of AI agents to make real-time decisions in the network, creating a very rich decentralized autonomous agent financial system.

Some projects have already begun to realize this vision, and we will introduce Autonomous Finance (AF for short), Dexi, and Outcome, among which the achievements of AF are the most eye-catching.

Autonomous Finance

AF focuses on researching and developing AI-integrated financial applications on AO. By building AI models and data-driven financial decisions on the AO chain, AF has made attempts to put intelligent decision-making on the chain. There are three main businesses, namely Core Infrastructure, AgentFi, and ContentFi.

The core infrastructure includes decentralized exchanges (DEX), lending, derivatives, and synthetic asset protocols.

AgentFi mainly refers to the execution of trading strategies through composable semi-autonomous and fully autonomous agents. Unlike other autonomous agent frameworks that rely on off-chain programs for signal processing and logic processing, AF’s autonomous agents use on-chain data flow for self-learning and execute investment strategies on various liquidity pools and financial bases within the AO ecosystem. These agents can operate autonomously without off-chain signals or human intervention.

Typical autonomous agents include:

• Dollar Cost Averaging (DCA) Asset Management Agent
• Self-balancing autonomous index fund
• Autonomous hedge funds with customized risk strategies
• Income Aggregation Agent
• On-chain Prediction Agent
• High Frequency Trading Agent

The DCA agent, as a basic agent, is often called upon to perform more complex agent execution logic, so as a frequently used composable agent module, there are many customizable parameters for users to adjust according to their own needs, such as triggering transactions within specific price ranges, adjusting the length of fixed interval trading time, asset price-weighted trading (such as buying more when the price is lower), as well as data-driven take profit and profit reinvestment signals.

The DCA agent application is built around two key AO processes:

• Agent process triggered by Cron (a time-based task management system commonly used to trigger task execution at a specific time): mainly responsible for conducting DCA transactions initiated by users and automatically scheduled, recording and managing funds, and timely updating the backend AO process
• Backend AO process: Manage proxy applications related to the username and track and record the transaction history of each proxy

The following diagram illustrates the design architecture and interaction components of the DCA agent.

For front-end users, the DCA agent’s front end is built on DEXI, and users can set up DCA agents by connecting the AO Connect wallet on the DEXI website. DEXI accesses information about available AMM pools and obtains the latest prices, while the DCA agent is responsible for executing specific trading logic. The backend AO process retrieves all agents related to the user.

Content Finance is a framework for attributing and monetizing data stored on the Arweave permanent network as composable assets for the AO process. AF is building applications that allow data contributors or content funds to contribute data to the permaweb, such as historical and real-time market intelligence. And these contents will serve as on-chain signals for autonomous agents and machine learning. For example, autonomous agents create new markets based on social media sentiment and historical data. Some examples:

• Monetizing data signals
• Content-Driven Financial Agent
• Subscription-based data recommendation agent
• Influential people contribute data to autonomous financial strategies
• The DAO and content fund related to data contribution aggregate various data sources to provide dynamic on-chain signals

Currently, AF has launched two main products, AO Link and Data OS.

AO Link is the message browser of the AO network, providing similar functionality to the traditional block explorer in blockchain systems. It includes message calculation function, graphic visualization of message links (clear and easy to understand), real-time message flow (latest information), and linked message list (easy for organization and navigation). Users can also view their token balances and message inbox. This tool provides a professional and efficient way to interact with and analyze the structure and activities of the AO network.

Data OS is a ContentFI protocol developed on the AO Network, which uses proprietary AI agents to retrieve content and generate content derivatives. Through this innovative approach, DataOS not only enhances the relevance and accessibility of content, but also establishes a reward mechanism for content creators. Currently, we can view various types of data on the AO Network in China, observe network activity, and temporarily not display various data related to content.

Dexi

Dexi is an important interactive interface for ordinary users to use proxies to participate in Agent Fi in AO. It is also an application implemented by agents on the AO network, which can independently identify, collect, and summarize various financial data of various events in the AO network (equivalent to Dexscrenner on AO). These data cover asset prices, token exchanges, liquidity fluctuations, and token asset features (such as detailed information on smart contracts). Dexi mainly serves two types of users: terminal users accessing the platform through web terminals, and AO applications that interact with Dexi by sending messages to utilize the collected data (understandable as Bot/Agent). As a core infrastructure, the main service provided by Dexi is data subscription service. Processes on the AO network can subscribe to the data stream of Dexi for a fee and immediately receive alerts such as price adjustments.

Outcome

Outcome is a prediction market built by the @puente_ai team, supported by @fwdresearch, @aoTheVentures, and @aoComputerClub. Outcome provides users with a platform to bet on various events, with current market prediction topics covering technology, memes, business, gaming, DeFi, and AO. The project claims that in the future, users will be able to make automatic bets in the prediction market by building autonomous agents based on real-world data and large language models.

AgentFi on AO provides us with a new perspective, exploring the direct deployment of AI models on the blockchain and using various AI agents to execute automated trades in the future. The limitations of traditional monolithic blockchains are broken by the novel underlying innovation of AO+AR. We look forward to seeing more applications on AO and cases of implementing financial strategies with AI agents.

Reference

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