Agent-Fi on AO: Fusion of AI Agent Financial Paradigm

Imagine in the future world, AI agent intelligent agents form a digital companion/symbiotic relationship with humans, autonomous agents can articulate intent in conversation and automatically decompose tasks and achieve expected results based on natural language requirements raised by the user.

AO has built an Actor-based asynchronous parallel network that does not achieve consensus on the entire contract calculation process, but achieves consensus on the transaction order. The optimistic default fixed transaction order ensures consistent execution results in the Virtual Machine. This choice allows AO network’s computing capacity to scale massively, supporting any type of computation. The AR network serves as the consensus layer for transaction order and the storage layer for transaction result status.

Compared with most of the current mainstream Block chain projects that serve as monolithic Block chains and only support Smart Contracts based on native state machines from the underlying level, AO’s infrastructure compatibility can support more complex computing power, including the operation of AI models.

After the recent update of the WASM Virtual Machine, AO’s 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 the Falcon series of Llama3 and many other models.

At the same time, AO uses WeaveDrive to allow users to access Arweave data inside AO just like accessing a local hard drive, and is compatible with highly heterogeneous processes of different types of Virtual Machines to interact in a shared environment, which means we have more long data sources and combination possibilities. This also means that in the future, when building applications, users’ motivation to upload data to Arweave increases because this data can also be used in AO programs. The AO development team has already uploaded model data worth $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 with integrated AI agents. By programming in AO, we create AI agents that make intelligent decisions in the market, and these agents may compete against each other or represent humans in their interactions. ‘When we look at the global financial system, approximately 83% of Nasdaq’s trades are executed by robots.’ Quantitative trading today is a precursor to AI agent trading, and in the future, the process of designing and selecting machine learning models to execute automated trades will be more easily ‘unboxed’ and automated by AI.

In the past few years, the development of Decentralized Finance has enabled various financial operations to be executed on-chain without the need for trust in centralized entities, such as lending, trading Tokens, or Derivatives. However, when we talk about the market, it is not only the reliability of these operations that matters. In fact, reliable execution of various operations is just 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 investments without doing all the research and participation yourself, you must find a reliable fund, trust them to manage your funds, and empower fund members to make smart decisions. But with the development of AO applications, we may be able to expand the smart decision-making part of the market, filtering information in the network, processing data, combining strategies, and integrating 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 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, making attempts to on-chain the intelligent decision-making layer by building AI models and data-driven financial decisions on AO on-chain. The main business consists of 3 parts, namely Core Infrastructure, AgentFi, and ContentFi.

The core infrastructure includes Decentralized Exchange (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 Agency
• Self-Balancing Autonomous Index Fund
• Autonomous Hedging Fund with Customized Risk Strategies
• Income Aggregator Agent
• on-chain prediction agent
• High-frequency trading agent

As a basic proxy, DCA proxy is often called upon when other more complex proxies execute logic. Therefore, as a frequently used composable proxy module, it has many customizable parameters for users to adjust according to their own needs, such as triggering trades in specific price ranges, adjusting fixed interval trade lengths, asset price-weighted trading (buying more as prices fall), and data-driven take profit and profit reinvestment signals.

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

• Proxy process triggered by Cron (a time-based task scheduling system commonly used for triggering task execution at regular intervals): mainly responsible for conducting user-initiated and automatic scheduled DCA transactions, managing recorded funds, and timely updating the backend AO process
• Backend AO process: Manage proxy applications related to usernames and track and record the transaction history of each proxy.

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

For users who use the front end, the front end of DCA Agent is built on DEXI. Users can set up DCA Agent by connecting the AO Connect wallet on the DEXI website. DEXI accesses information about available AMM pools and obtains the latest prices, while DCA Agent is responsible for executing specific transaction 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 Agency
• Data recommendation agent based on subscription
• 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, namely AO Link and Data OS.

AO Link is the message browser of the AO network, providing similar functionality to the Block Explorer in traditional blockchain systems. It includes message computation, clear and understandable graphical visualization of message links, real-time message flow for the latest information, and a linked message list for easy organization and navigation. Users can also view their Token balances and message inboxes. 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 acquire content and generate content derivatives. Through this innovative approach, DataOS not only enhances the correlation and accessibility of content, but also establishes a reward mechanism for content creators. Currently, we can view various data on the AO Network, observe network activity, and various data related to content are not currently displayed.

Dexi

Dexi is an essential interactive interface for regular users to participate in Agent Fi through proxies in AO. It is also an application implemented by proxies on the AO network, which can autonomously 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 characteristics (such as smart contract details). Dexi primarily serves two types of users: end users accessing the platform through web terminals and AO applications that interact with Dexi by sending messages to utilize the collected data (understood as bots/agents). As a core infrastructure, Dexi primarily provides data subscription services. Processes on the AO network can pay for the subscription of Dexi’s data stream and immediately receive alerts for price adjustments and other updates.

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 place bets on various events. Currently, the prediction topics in the market cover technology, memes, business, games, Decentralized Finance, and AO. The project claims that in the future, users will be able to make automatic bets on prediction markets by building self-governing agents based on real-world data and large language models.

AgentFi on AO provides us with a new perspective, exploring the future of on-chain Block AI model deployment and using various AI agents to execute automated transactions directly. The limitations of traditional single-block chains are broken by the novel underlying innovation of AO+AR design. We look forward to seeing more on-chain AO applications and cases of implementing financial strategies with AI agents.

Reference

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