AI Agents and Blockchain Integration: AO Network Unveils a New Financial Paradigm

In the future world, AI agent entities may form a digital symbiotic relationship with humans. These autonomous agents are capable of clarifying intentions in conversations based on users' natural language needs and automatically breaking down tasks to achieve the desired outcomes.

The AO network has established an Actor-based asynchronous parallel network that employs an innovative consensus mechanism. This mechanism does not reach consensus on the entire computation process of contracts but only on the order of transactions, optimistically assuming that the results of running the fixed transaction order in the virtual machine are consistent. This design allows the computing power of the AO network to scale massively, ultimately supporting any type of computation. In this architecture, the AR network is used as the layer for reaching consensus on transaction order and as the storage layer for the status of transaction results.

Compared to other mainstream blockchain projects, AO's infrastructure has stronger compatibility, capable of supporting more complex computing power, including the operation of AI models. Recently, after the update of the WASM virtual machine, AO's computing units can access 16GB of memory, which means that 16GB models can be downloaded and executed on AO, sufficient to run large language model computations, such as various models in the Falcon series of the unquantized version of Llama 3.

AO also employs WeaveDrive technology, allowing users to access Arweave data within AO as if they were accessing a local hard drive. This design enhances the possibilities for data sources and combinations, while also increasing users' motivation to upload data to Arweave, as this data can be utilized within the AO program.

The system design of AO makes it possible to implement smart contracts that integrate AI agents. By programming in AO, we can create AI agents that make intelligent decisions in the market, which may be adversarial to each other or represent humans against humans. In the future, the process of designing and selecting machine learning models and executing automated trading may become easier for AI to "unbox" and automate.

The development of DeFi has made it possible to execute various financial operations on-chain without relying on centralized entities. However, the core factor that determines whether a market is vibrant remains the flow of capital, as well as the individuals who decide to buy, sell, lend, or participate in various financial games. With the development of AO applications, we may be able to expand the intelligent decision-making aspect of the market, filtering information, processing data, and combining strategies within the network, integrating the wisdom of AI agents to make real-time decisions in the network, creating a rich decentralized autonomous agent financial system.

Currently, some projects have begun to realize this vision, including Autonomous Finance (AF), Dexi, and Outcome.

Autonomous Finance

AF focuses on researching and developing financial applications that integrate AI on AO, attempting to bring intelligent decision-making on-chain by building AI models and data-driven financial decisions on the AO Blockchain. AF's main businesses include core infrastructure, AgentFi, and ContentFi.

Core facilities include protocols such as decentralized exchanges, lending, derivatives, and synthetic assets.

AgentFi primarily implements trading strategy execution through composable semi-autonomous and fully autonomous agents. The autonomous agents provided by AF use on-chain data streams for self-learning and execute investment strategies across various liquidity pools and financial foundations within the AO ecosystem. These agents can operate independently without off-chain signals or human intervention.

Typical autonomous agents include:

• Dollar Cost Averaging (DCA) Asset Management Agency
• Self-Balancing Autonomous Index Fund
• Autonomous hedge funds with customized risk strategies
• Yield Aggregation Proxy
• On-chain prediction agency
• High-frequency trading agent

The DCA agent, as a basic agent, is often called upon when other more complex agent execution logics are in operation. It has many customizable parameters for users to adjust according to their needs, such as triggering trades within a specific price range, adjusting the fixed interval trading time length, and asset price weighted trading, etc.

Content finance is a framework for attributing and monetizing data stored on the Arweave permanent network as composable assets for AO processes. AF is building applications that allow data contributors or content funds to contribute data, such as historical and real-time market intelligence, to the permaweb. This content will serve as on-chain signals for autonomous agents and machine learning.

Currently, AF has launched two main products: AO Link and Data OS. AO Link is the message browser of the AO network, providing functions similar to those of a block explorer in traditional blockchain systems. Data OS is the ContentFI protocol developed on the AO Network, utilizing autonomous AI agents to acquire content and generate content derivatives.

Dexi

Dexi is an important interactive interface for ordinary users to participate in Agent Fi using proxies in AO. It is an application implemented by proxies on the AO network that can autonomously identify, collect, and aggregate financial data from various events within the AO network. This data includes asset prices, token exchanges, liquidity fluctuations, and token asset characteristics. Dexi mainly serves end-users who access the platform via web terminals and AO applications that interact with Dexi by sending messages to utilize the collected data. As a core infrastructure, Dexi primarily provides data subscription services.

Outcome

Outcome is a prediction market platform that offers users the opportunity to bet on various events. Currently, the prediction topics in the market cover technology, memes, business, gaming, DeFi, and AO. The project plans to allow users to build autonomous agents based on large language models in the future, utilizing real-world data for automated betting in the prediction market.

AgentFi on AO provides us with a new perspective to explore the future of deploying AI models directly on the Blockchain and using various AI agents to perform automated trading. The limitations of traditional monolithic Blockchains are broken by the innovative underlying design of AO+AR. We look forward to seeing more applications on AO and cases of combining AI agents to realize financial strategies.