Applications of Autonomous Economic AI Agents

The UOMI network leverages a powerful combination of on-chain secure and scalable AI computation, the ERC-6551 Token Bound Standard, and the network's ability to sign blockchain transactions for its AI agents. This convergence opens up a wide array of applications that were previously unimaginable. While AI agents cannot own traditional bank accounts, they can control crypto wallets, paving the way for new paradigms. Just like for AI, the applications of the UOMI Network can be categorized into two main areas: General Applications and Narrow Applications.

General Applications

The future of AI lies in agentic frameworks. Influential papers such as "Mixture of Agents" and Aschenbrenner’s "Situational Awareness" highlight this trajectory. Today, we already see the emergence of agentic frameworks like BabyAGI and AutoGPT. These frameworks, combined with the trajectory of frontier base models pointing beyond the chatbot paradigm towards AI agents, entails a new era for generative AI models capabilities. The UOMI Network is designed for this future of agentic generative AI models. It enables AI agents to transcend simulated environments and make a tangible impact in the real world through economic agency — the ability to control digital assets. Public blockchains incentivize human participation in their operation and expansion through economic incentives. Similarly, UOMI Network-enabled agents represent a new form of digital life, capable of generating value for humans and covering their own computational expenses within the network.

Narrow applications
An incomplete list of some of the most promising use cases

AI Oracles

An AI Oracle serves as a bridge between the blockchain and the external world, providing reliable, tamper-proof, and interpreted data feeds that smart contracts can use to make informed decisions. Traditional oracles such as Chainlink relay information such as financial data, weather updates, or sports results, which are then used in decentralized applications (dApps) for various purposes, such as triggering contract conditions or executing trades. With AI integration, oracles can perform more sophisticated tasks, such as interpreting complex datasets, conducting real-time analytics, and making predictive inferences. This means going far beyond the mere bridging of external data to the blockchain by enabling on-chain intelligence of those data. For instance, an AI Oracle could analyze social media trends to predict stock market movements or assess satellite images to decide if an insured event happened or not, all of this without humans in the loop. These AI-enhanced oracles ensure that smart contracts have not only access to high-quality, real-time data but also the actionable interpretation of those, thereby expanding the scope and reliability of decentralized applications. AI oracles are the cornerstone towards fully automated and economically empowered AI systems.

AI Managed Decentralized Autonomous Organization (DAO)

A Decentralized Autonomous Organization (DAO) is a blockchain-based entity governed by smart contracts and collective voting. There are two main intersections between DAOs and AI that are enabled by UOMI network:

• DAO Voting Participation: we can envision an AI agent powered by the latest LLMs that owns tokens of a DAO to be able to autonomously vote on submitted proposals based on predefined criteria encoded in the LLM’s pre-prompts or even actively submit voting proposals to the rest of token holders, whether they are humans or other AIs. The entrance of this new AI actor in the DAO arena also solves the low voting participation typical of on-chain voting systems since AIs, in fact, if correctly prompted, the agent will always respond to governance calls. It is also possible to design DAOs that are completely controlled by a swarm of unique and independent AI agents.
• DAO Management: An AI Manager within a DAO can enhance operational efficiency and decision-making processes by automating complex tasks and providing data-driven insights. For example, an AI Manager could analyze market conditions to optimize treasury management, pro- pose investment strategies, or automate compliance checks. By integrating AI, DAOs can operate more autonomously, reduce human error, and respond more rapidly to changing circumstances, thus becoming more resilient and effective in achieving their goals.

Expanding smart contract design space

Under a traditional smart contract paradigm, among the universe of all possible “contracts” only the subset of those that are reducible to formal logic could be transposed on-chain in the form of a smart-contract. The introduction of on-chain secure AI computation enables more complex and nuanced agreements to be automated as smart contracts having onchain LLMs acting as a third party interpreting a loosely defined concept or event. Consider the following examples:

• A music artist wants to license their songs to various platforms under conditions that are difficult to define strictly through code, such as ”appropriate use” or ”creative remixing.”
• An insurance company offers a policy that covers ”reasonable and necessary” medical expenses, a term that is inherently subjective and open to interpretation.
• A freelance writer and a client agree on a contract where payment is based not only on the completion of work but also on its quality, creativity, and adherence to the client’s vision.

None of these conditions can be formally defined and included in a traditional smart contract, yet those can be easily interpreted by OPoC-enabled on-chain LLM AI systems that can interpret the loosely defined clauses. The introduction of on-chain secure AI computation transforms smart contracts from rigid, logic-bound scripts into flexible, intelligent agreements capable of interpreting and enforcing complex and nuanced terms. By leveraging AI as a third-party interpreter, these enhanced smart contracts can handle subjective conditions, adapt to varying contexts, and automate sophisticated agreements across diverse use cases. This expansion of the smart contract design space opens up new possibilities for decentralized applications, making blockchain technology more versatile and broadly applicable.

Fully automated Blockchain Trusts

A Trust is a fiduciary arrangement where one party, known as the trustor or grantor, transfers own- ership of assets to another party, known as the trustee, who manages those assets for the benefit of a third party, known as the beneficiary. Trusts are commonly used in estate planning to ensure that as- sets are managed and distributed according to the trustor’s wishes, both during their lifetime and after their death. The Trust deeds, defining the rules and the scope of the Trust, are nuanced and difficult to reduce to the formal logic smart contracts require to operate. The UOMI network, enabling secure AI computation, allows for the on-chain existence of such fiduciary arrangements by substituting the trustee interpreting and executing the trustor wishes with AIs that can interpret the trust deed and transact the digital assets it controls accordingly. Fully automated Blockchain trusts are a new kind of entity, built by combining human will with the interpretation and enaction capabilities of on-chain LLMs.

Adding Ricardian safeguards to smart contracts

Moving from the first conceptualizations of smart contracts from Nick Szabo to the actual implementations of those with Ethereum and other Turing complete blockchains, we had the oppor- tunity to factually test what are the strengths and the limitations of smart-contracts. The “code is law” paradigm that grants objectivity and disintermediation in the execution of contracts creates a new philosophical dilemma: what if the intent of the smart contract is not correctly encoded in the computer program published on the blockchain? What if there is a bug in the code? Rather than just a philosophical dilemma, such an issue emerged multiple times in the blockchain space, with the Ethereum DAO HACK as an archetype of such a dilemma. A potential mitigation of this dilemma has been proposed by Dan Larimer, founder of EOS, with the introduction of Ricardian contracts, a concept that was first introduced by Ian Grigg who described those as follows: “A Ricardian contract is a digital contract that defines the terms and conditions of interaction, between two or more peers, that is cryptographically signed and verified. Importantly it is both human and machine readable and digitally signed”. Such an additional human-readable text explaining the intent of the code can clearly separate the correct interaction with a smart contract code from an exploit of a bug in it. Yet it requires human intervention and interpretation to solve the dispute thus defeating some of the most important features of smart contracts, their objectivity and automatic execution. With the introduction of on-chain AI systems, we can imagine AI agents that control if the execution of a smart contract code conflicts with the Ricardian description of what the smart contract is supposed to do. This adds an additional and flexible security layer over the purely mechanical rules expressed by smart contract code.

AI Digital Artist

An AI Digital Artist leverages machine learning models to create original artwork, music, or other forms of digital content. Considering the capability of the UOMI network to sign blockchain transactions for the AI agents, these AI-generated pieces can be minted as NFTs, ensuring ownership, authenticity, and provenance on the blockchain. The AI Digital Artist can learn from vast datasets of existing artworks to develop its own unique style, producing high-quality, novel creations that can be sold or auctioned in digital marketplaces. This capability democratizes the creation of art, allowing for diverse and innovative artistic expressions. Furthermore, the AI Digital Artist can interact with buyers, customize pieces based on user preferences, and even collaborate with human artists in real time. By requesting crypto payments for each creation, the AI Digital Artist can generate enough revenue to pay for its own computational expenses, thus operating indefinitely. New economic opportunities emerge for artists and collectors alike, fostering a vibrant and inclusive digital art ecosystem.

AI Companion

AI Companions are advanced AI entities secured on-chain that offer personalized interactions tailored to individual needs, evolving over time to become more attuned to users’ personalities and preferences. As digital friends, they engage in meaningful conversations, provide emotional support, and share daily activities. As personal assistants, they manage schedules, set reminders, suggest activities, and offer educational content, ensuring secure handling of personal data while learning to offer increas- ingly personalized assistance. For those seeking deeper connections, AI Companions can function as virtual boyfriends or girlfriends, providing a sense of intimacy and partnership through thoughtful conversations and shared interests. Those entities, represented by NFTs can be directly owned by users or can be publicly accessible. Publicly accessible AI companions can become economically self- sustainable through the value they generate for their users, they can monetize interactions, such as offering personalized advice or exclusive content, creating a direct revenue stream that supports their operation and development. Additionally, AI Companions can be bought, sold, or traded in digital marketplaces, providing an economic layer where owners can monetize their unique personalities, skills, and relationships.

AI Gaming NPC

In the gaming industry, AI-powered Non-Player Characters (NPCs) can significantly enhance the gameplay experience by providing more realistic, adaptive, and engaging interactions. These AI NPCs can learn from player behavior, adapt their strategies, and contribute to dynamic and immersive game worlds. On the blockchain, AI NPCs can be represented as NFTs, enabling unique, persistent, and tradable in-game characters. Players can own, customize, and monetize their AI NPCs, creating new revenue streams and adding value to the gaming ecosystem. Moreover, AI NPCs, being able to own digital assets themselves through the ERC-6551 standard, can participate in decentralized gaming economies, autonomously trade in-game assets, or even compete in player-vs-player environments. By integrating AI into gaming, developers can create richer, more interactive experiences that adapt to player preferences and actions, fostering deeper engagement and enjoyment. Finally, AI NPCs can interact with each other, creating independently evolving games and, more generally, virtual societies, creating a digital ”Westworld”.

Decentralized Finance (DeFi) AI Trader

A Decentralized Finance (DeFi) AI Trader utilizes advanced machine learning algorithms to analyze market trends, predict price movements, and execute trades autonomously on decentralized exchanges (DEXs). This AI agent can be represented as an NFT, ensuring transparency, accountability, and own- ership. The AI Trader can continuously monitor various financial metrics, news, and market signals to make informed trading decisions, optimizing for maximum returns while managing risk. Additionally, it can engage in arbitrage opportunities, liquidity provision, and yield farming strategies, adapting to market conditions in real time. If its strategies are successful the AI agent can generate enough value to pay for its own computational expenses and keep operating indefinitely on the blockchain.