Why AI Agent Will Become the Next Hot Trend Narrative in Web3

A Short Project Analysis in Web3 Ecosystem, June 2024

A General Intro

What is an AI Agent?

Imagine you designed a video game like Mario Kart. As an AI Agent, Mario plays a crucial role in the game’s outcome. He must continuously make decisions throughout the game, such as when to accelerate, turn, and jump to avoid obstacles, overtake other racers, and ultimately win the race.

In this example, the entire game environment and the actions of other racers constitute the Agent’s environment. The Agent makes optimal decisions based on this information. For instance, the Agent will turn left if the road ahead turns left.

The essence of an AI Agent is a system that can perceive its environment, make decisions based on given goals, and take action. These decisions are based on the Agent’s understanding of the environment, such as its destination, current location, and any obstacles around it.

One of the most impressive characteristics of an AI Agent is its adaptability. It can ‘learn,’ meaning it can continuously improve its decision-making process through interactions with the environment. For example, it might learn how to avoid obstacles better or find the best racing lines after multiple games, showcasing its ability to evolve and adapt.

In summary, an AI Agent is an AI system capable of perceiving and understanding its environment, making decisions, taking actions, and continuously learning and evolving.

AI Agent — The Coming Trendy Narrative

The next stage in developing large language models is heading in two directions. One direction is “downward,” focusing on updating the underlying system. The other is “upward,” which involves enhancing productivity by increasing complexity, intelligence, and automation from a higher dimension — essentially, developing the Agent model. A standard single-agent module includes memory, planning, execution, and reflection components, making it more intelligent than the traditional interaction model of large language models.

Moreover, people are constantly adding desirable features to suit their needs, expanding the boundaries of imagination. Some fantastic ideas include integrated search engines and compiler functions, which have been realized one by one in recent research months.

While a single Agent is powerful and easy to use, OpenAI GPTs represent the future AI business model based on single Agents. In this model, developers and users come together to create and purchase the GPTs they desire. OpenAI and developers then share the profits. This collaborative business model fosters community, bringing together communities, developers, platforms, and users in a shared vision of AI advancement.

However, let’s not limit our vision to the current possibilities. The future of AI is boundless, and our imagination can stretch even further. The potential for AI evolution is vast, and this limitless potential should inspire and excite us.

For instance, can the Agents within GPTs learn and evolve independently?

Challenges of AI in Web3

In a nutshell, the biggest problem in Web3 is that there hasn’t been one project realistically designed for adopting the users in the web3 industry. When one mentions users, it means the mass crowd who don’t know sophisticated trading strategies, use Rust language to create a program or learn how to fetch accurate data from platforms like Massari, Dune, or DeBank. These users should be acquired and captured in the beginning phase because they are the majority user base in the web3 world.

A Straight Shot

Trading is closely related to money because money is a universal medium of exchange, a unit of account, and a store of value, which are all integral to trading activities. Here’s why:

1. Medium of Exchange: Facilitates Transactions: Money simplifies trading goods and services by providing a common medium for exchange for various items. This eliminates the complexities of barter systems, where a direct exchange of goods or services is required.
2. Unit of Account: Standardized Value Measurement: Money provides a consistent measure of value, making it easier to compare the worth of different goods and services. This standardization is essential for setting prices, negotiating trades, and accounting.
3. Store of Value: Preserves Wealth: Money allows individuals and businesses to store wealth in a form that maintains its value over time. This is crucial for trading because it enables participants to save and accumulate resources to facilitate future trades.
4. Economic Efficiency: Ease of Use: Money is highly liquid, meaning it can be easily and quickly converted into goods and services without losing value. This liquidity makes money an ideal tool for trading, ensuring that trades can be executed efficiently.
5. Trust and Credibility: Reduces Transaction Costs: Using money significantly reduces transaction costs associated with trading. These include the costs of finding a trading partner, negotiating terms, and executing the exchange.
6. Market Functioning: Confidence in Value: Money issued by a recognized authority (e.g., government) carries trust and credibility that facilitates trading. People are more willing to trade when they have confidence in the currency’s stability and value.

Trading Bots as Leverage

Phrase I, adding dishes to the plate

Crypto AI trading bots use AI and ML to analyze crypto market data, employing strategies like trend following and arbitrage to exploit market inefficiencies. They integrate risk management for loss reduction and execute trades quickly via HFT. Despite their potential benefits, they face challenges, including regulatory compliance, technical issues, and market risks, necessitating careful user oversight.

The Downsides of Trading Bots:

• Overfitting: AI trading bots can be prone to overfitting, performing exceptionally well on historical data but failing to generalize to new market conditions. This can lead to poor performance when deployed in real-time trading environments.
• Lack of Adaptability: Markets are dynamic and constantly changing, and AI trading bots may struggle to adapt to sudden shifts or unexpected events that deviate from their trained patterns.
• Data Quality and Bias: The data quality used to train AI trading bots is crucial. Biases or inaccuracies in the training data can lead to flawed decision-making. Additionally, the historical data may not always capture all relevant market dynamics, leading to incomplete or biased models.
• Dependency on Market Conditions: AI trading bots may perform well in certain market conditions but poorly in others. For instance, a bot trained during a bull market may struggle to perform during a bear market or periods of high volatility.
• Risk Management: AI trading bots often require sophisticated risk management strategies to mitigate potential losses. They can expose investors to significant financial risks without proper risk controls, especially during turbulent market conditions.
• Regulatory Compliance: Regulatory frameworks governing financial markets may pose challenges for AI trading bots, particularly regarding transparency, accountability, and compliance with regulations designed to protect investors.
• Technical Issues: Like any software, AI trading bots are susceptible to technical glitches, bugs, or failures. A malfunctioning bot can result in significant financial losses if not promptly detected and addressed.
• Black Box Nature: Some AI trading algorithms operate as black boxes, meaning their decision-making processes are not transparent or easily interpretable. This lack of transparency can be a barrier to adoption, as investors may hesitate to entrust their funds to systems they don’t fully understand.
• Human Oversight: Human oversight is still essential despite the automation provided by AI trading bots. Overreliance on AI without human intervention can lead to catastrophic errors, especially in complex or unforeseen situations.
• Market Impact: In highly liquid markets, large-scale automated trading by AI bots can impact market dynamics, potentially leading to market inefficiencies or increased volatility.

Solutions

Phrase I: AI Agents+ Trading Bots

Combining AI agents with trading bots creates advanced tools for automated trading. These bots enhance decision-making by analyzing vast amounts of data and recognizing patterns, continuously improving through adaptive learning. They execute trades quickly and efficiently, operate 24/7, and offer advanced risk management by assessing real-time risk and detecting anomalies. Customizable and flexible, they can follow tailored strategies and trade across multiple asset classes. Integration with technologies like blockchain and IoT further enhances their capabilities. This combination democratizes sophisticated trading strategies, making them accessible to individual investors and providing educational insights into market dynamics.

The Importance of Data

Data is crucial for Web3 users because it empowers them with ownership and control, allowing for self-sovereign identity and direct monetization. Web3 ensures data transparency, security, and interoperability, fostering trust and reducing the risk of fraud. Users benefit from personalized experiences and explicit consent for data use. Additionally, Web3’s decentralized nature and open standards enable new business models and innovation, creating a collaborative ecosystem where data is a valuable, secure, and user-controlled asset.

Data Agent

• Phase II, Polishing the product

Understanding Web3 data can be challenging for beginners due to its decentralized nature and complex technologies. Firstly, Web3 operates on blockchain networks, which store data across a distributed ledger maintained by multiple nodes. This decentralized architecture contrasts with traditional centralized systems, where data is stored in a single location, making it easier for beginners to grasp the concept of distributed data storage and retrieval. Additionally, Web3 data is encoded in formats like hexadecimal strings and encoded transactions, which can be cryptic and unintuitive for newcomers unfamiliar with blockchain technology.

Secondly, Web3 data often involves cryptographic principles and protocols, adding another layer of complexity for beginners. Concepts like public-private key cryptography, hash functions, and digital signatures are fundamental to Web3 data security and integrity but may be challenging for newcomers to understand. Moreover, Web3 platforms utilize advanced technologies such as smart contracts, decentralized identifiers (DIDs), and consensus mechanisms like proof-of-work or proof-of-stake, further complicating the comprehension of Web3 data for beginners. Overall, the decentralized, cryptographic, and technologically advanced nature of Web3 data presents significant hurdles for beginners seeking to understand and navigate the intricacies of the decentralized web.

Adopting the Web2

Adopting Web2 users to Web3 is crucial for several reasons. Firstly, Web3 represents a paradigm shift towards decentralization, empowering users with greater control over their data, identities, and digital assets. By transitioning Web2 users to Web3, we democratize access to technology and promote user sovereignty, allowing individuals to reclaim ownership of their online presence and transactions. This shift aligns with growing concerns about data privacy, censorship, and surveillance in centralized platforms, offering users an alternative that prioritizes transparency, security, and user empowerment. Moreover, transitioning Web2 users to Web3 fosters innovation and competition in the digital space, driving the development of decentralized applications (dApps) and blockchain-based services that offer novel functionalities and business models.

Secondly, adopting Web2 users to Web3 facilitates the broader adoption and mainstream acceptance of decentralized technologies. Web3 can potentially revolutionize various sectors beyond finance, including healthcare, supply chain management, voting systems, and content creation. By onboarding Web2 users to Web3, we expand the user base and ecosystem of decentralized applications, a network effect that accelerates the growth and maturity of the decentralized web. This increased adoption not only validates the utility and viability of Web3 but also incentivizes further investment, research, and development in decentralized technologies, driving innovation and fostering a more resilient and inclusive digital infrastructure for the future.

Mass Adoption (Market Transition)

Phrase III

In the current landscape, an array of teams is diligently engaged in developing Web3 AI agent projects, albeit discreetly. As the competitive dynamics intensify within this burgeoning sector, strategic foresight becomes paramount for navigating the increasingly tumultuous waters. It is evident that in such an environment, efficiency hinges on making calculated market transitions that prioritize the cultivation of a singular, exceptional AI agent project rather than dispersing resources in an attempt to serve as the central repository for myriad AI agent endeavors.

This approach recognizes the multifaceted nature of the competitive landscape, wherein differentiation and innovation are prerequisites for market ascendency. By directing focus toward the development of a standout AI agent project, organizations can position themselves at the forefront of the evolving Web3 paradigm. Moreover, this strategic imperative extends beyond merely catering to the needs of the Web3 community; it encompasses a broader vision of transitioning and serving Web2 users worldwide. By embracing this comprehensive strategy, entities can capture market share in the nascent Web3 ecosystem and facilitate a global audience’s seamless integration and adoption of decentralized technologies, thereby solidifying their position as industry leaders in the transformative digital landscape.

Unlimited Options

Combining AI agents with Web3 technologies creates a powerful synergy that enhances the capabilities of decentralized applications (dApps), improves user experiences, and fosters innovation. Here’s how AI agents can integrate with Web3:

1. Decentralized Autonomous Organizations (DAOs):

• Intelligent Decision-Making: AI agents can analyze data and provide insights to assist DAOs in making more informed and efficient decisions.
• Automated Governance: They can automate governance processes, such as voting and proposal evaluation, making DAOs more responsive and effective.

2. Decentralized Finance (DeFi):

• Automated Trading and Investment: AI agents can manage decentralized trading bots that execute trades based on real-time market analysis, improving trading efficiency and profitability.
• Risk Assessment: They can analyze financial data to assess risks and optimize investment strategies, providing more secure and profitable DeFi services.

3. Smart Contracts:

• Enhanced Automation: AI agents can trigger and manage smart contracts based on complex conditions and data inputs, automating more sophisticated and nuanced processes.
• Predictive Analytics: They can use machine learning models to predict outcomes and optimize the execution of smart contracts.

4. Personalized dApps:

• User Customization: AI agents can personalize user experiences in dApps by tailoring services and recommendations based on user behavior and preferences.
• Chatbots and Virtual Assistants: Integrating AI-powered chatbots can enhance user support and interaction within decentralized platforms, providing real-time assistance and information.

5. Data Privacy and Security:

• Anomaly Detection: AI agents can monitor blockchain transactions and user activities to detect and prevent fraud or security breaches.
• Enhanced Privacy: They can help manage and anonymize data, ensuring user privacy while maintaining the integrity and transparency of blockchain systems.

6. Interoperability and Scalability:

• Cross-Chain Communication: AI agents can facilitate interoperability between blockchain networks, enabling seamless data and asset transfers across chains.
• Scalability Solutions: They can help optimize transaction processing and resource allocation, improving the scalability of decentralized networks.

7. Supply Chain and Provenance:

• Traceability: AI agents can enhance supply chain dApps by analyzing and verifying data across different stages, ensuring the authenticity and provenance of goods.
• Efficiency Optimization: They can optimize supply chain operations by predicting demand, managing inventory, and reducing delays.

8. Content Creation and Curation:

• Automated Content Generation: AI agents can create and curate content for decentralized platforms, such as social media dApps, ensuring relevant and engaging user experiences.
• Content Moderation: They can help moderate content in a decentralized manner, identifying and filtering out inappropriate or harmful material.

9. Healthcare and Identity Management:

• Health Data Analysis: AI agents can analyze decentralized health data for personalized healthcare solutions and predictive health monitoring.
• Self-Sovereign Identity: They can manage and secure digital identities on the blockchain, giving users control over their personal information and credentials.

Combining AI agents with Web3 enhances decentralized applications by improving decision-making, automating processes, personalizing user experiences, enhancing security, and ensuring interoperability. This integration leverages the strengths of both AI and blockchain technologies to create more intelligent, efficient, and user-centric decentralized ecosystems.

AI Web3 Landscape

Current AI Project in Web3

Link to “Web3+ AI Portfolio Research” (My Previous Research)

There are currently at least more than 140 Web3 + AI concept projects in the industry, and a total of 85 projects have issued tokens, and some will issue tokens next year. These 140 projects cover infrastructure, data, prediction market, computing and computing power, education, DeFi & cross-chain, security, NFT & games & metaverse, search engine, social & creator economy, AI chatbot, DID & Messaging, governance, trading robots, and many other directions.

Active VC firms include a16z Crytpo, Jump Crypto, Hashed, DWF Labs, Foresight Ventures, HashKey Capital, Binance Labs, CoinFund, IOSG Ventures, SNZ, Variant, 1kx, Bankless, Hash Global, Animoca Brands, Coinbase Ventures, Galaxy Digital, Sequoia, GSR, Dragonfly, and many more. In the eyes of CoinFund, AI is the fastest-growing field next year, and Messari is firmly optimistic about combining AI and cryptocurrency.

AI Agents Transforming the Web3 User Experience

From the Biconomy: Article

Autonomous Portfolio Managers:

These are AI Agents that can be leveraged to manage a pool of assets from multiple different user profiles. The AI Agent will aim to maximise the pool’s earnings by delegating the assets across various DeFi strategies and leveraging the right set of off-chain AI data streams. It’s essentially a portfolio management service leveraging the power of AI. To make sure that the protocol is trust minimized, projects are also enabling ZK (via protocols like Modulus) to provide the on-chain proofs of the AI inferences they generate in the process.

Prompts-based AI Agents:

Imagine a future where you could simply tell an AI agent what you want to achieve on-chain, and it would automatically compose and execute the necessary transactions for you.

This is what the majority of AI Agent projects are thriving to build, and we can imagine a future where prompts might become the preferred way for general users to interact with the blockchains.

‍Projects like Wayfinder, Brian Knows, Aperture Finance, and others are developing ChatGPT-like interfaces that can help users make knowledgeable transactions on the blockchain directly by chatting with the AI Agent. These protocols leverage the power of LLMs to convert users’ prompts and intents into executable transactions.

Autonomous Agents

Autonomous is a platform that enables the creation and management of autonomous agent services. These services, known as agent services, operate independently off-chain as a multi-agent system (MAS), collaborating to achieve shared objectives. In essence, Autonolas empowers developers to build and deploy autonomous agents that work together seamlessly off-chain while leveraging blockchain technology for enhanced capabilities on-chain. One such agent is BabyDegen.

‍Here is a directory of other agents being built on Olas Network — https://pandora.computer/

‍AutoTX by Polywrap

Polywrap is building a network of specialized AI agents that perform complex tasks for web3 users and protocols. These agents leverage crowdsourced insights, off and on-chain data sources, task planning, and batch transactions to efficiently solve problems and make decisions across various domains. Current agents include those for payments, market research and trading, social content curation, predictions, and public goods funding. Future plans for Polywrap involve expanding the range of specialized agents, decentralizing their execution, and evolving the system through community-driven governance. One such AI Agent is AutoTx.

AutoTx can translate high-level user goals into a series of blockchain transactions. This means you no longer need to be an expert in every protocol or spend hours learning how to manually compose different types of transactions. Simply tell AutoTx what you want to achieve, and it will handle the rest.

Parallel Colony

Parallel Studios is taking a fresh approach to AI agents with Colony, a new AI-powered Web3 survival game. In Colony, highly autonomous AI agents, or “avatars,” continuously learn from their environment. Players must guide and collaborate with these avatars, which have diverse skills and abilities, to survive in a future Earth with competing colonies.

Colony stands out by integrating continuous learning into its gameplay. The AI avatars develop unique personalities and worldviews, learning from their own experiences, identities, and goals. Additionally, these avatars can autonomously manage digital assets through dedicated Web3 wallets, allowing them to trade with other in-game avatars.

Whitepaper: https://paper.parallel.life/colony_paper_v1.pdf

Wayfinder

Wayfinder is creating a “map” for AI agents to handle tasks and simplify on-chain activities for users. By open-sourcing development and incentivizing builders with the $PROMPT token, Wayfinder will expand a network of navigational instructions. Wayfinder paths will continuously enhance AI agents’ capabilities, making them smarter over time. It aims to connect blockchains and off-chain data sources, allowing users to execute tasks easily via command prompts. Their innovation aims to make blockchain interactions more efficient and accessible, improving users’ lives by reducing complexity and stress. You’ll love this analogy and explanation by @tiggity_tc on Wayfinder.

This is a video of the wayfinder agent in action: https://x.com/AIWayfinder/status/1779605000735367549

‍Whitepaper:

https://paper.wayfinder.ai/wayfinder_paper_v1.pdf

Noya

NOYA is a decentralized finance (DeFi) protocol that empowers AI agents to manage liquidity across multiple blockchains with security and precision. It uses a composable system built from the ground up, including a private keeper network, an AI-compatible oracle, and a competitive environment for AI and strategy managers. Noya has multiple vaults, each catering to a separate user intent profile. The protocol has its own designed AI Oracles to read various DeFi markets and pass information to the AI Agent.

NOYA’s infrastructure supports multiple intents like liquidity provisioning, leverage management, and borrowing rate optimization, using advanced technologies such as Zero-Knowledge Machine Learning (ZKML). It aims to set a new standard for omnichain liquidity management and financial strategies. The team is closely rolling out access to the protocol.

Brian Knows

Brian offers APIs which developers can integrate into their applications to provide users with the ability to generate web3 transactions by passing in their intents via prompts like “Can you swap 10 usdc for eth on Uniswap on Ethereum mainnet?” They also provide smart contract deployment services via prompts. On the backend, team uses LLMs to convert the prompts into a web3 transaction and then get it executed via their preferred protocol and solver integrations.

The team has also built out a Brian app that you can use to explore the feature set. The team is looking to expand their offerings by providing recurring and automated payment setup optionalities to their users, and much more.

Aperture Finance

Aperture Finance revolutionises DeFi by offering liquidity management services through a user-friendly protocol. It aims to enhance the DeFi user experience with an intuitive chatbox interface inspired by GPT, allowing users to express their goals in natural language. Third-party participants, known as Solvers, handle requests by optimising processes to ensure efficient and cost-effective execution.

Fungi Agent

Fungi leverages the power of smart accounts and account abstraction to provide a self-custodial AI Agent experience . Fungi lets users instruct command prompts through its interface, it then processes real-time blockchain data and autonomously executes actions based on user’s instructions.

Users can chat with Fungi to deepen their understanding of crypto, receive personalized guidance, perform on-chain transactions, create customized DeFi strategies (Hyphas), and even monetize these Hyphas by sharing them with the community. Fungi works as a network of agents who interact with each other and learn from past experiences — a financial superintelligence accessible to everyone.

Here is how Fungi works:

Source: https://words.odisealabs.com/defi-ai

Fyde Protocol

Fyde enables users to grow their crypto holdings faster by depositing into diversified, AI-managed vaults that lock in gains and reallocate assets based on market performances and reduced volatility.

Users can deposit various tokens into these vaults and receive $TRSY, a token representing their share of the vault’s assets. Fyde aims to maintain consistent $TRSY liquidity across market conditions, making it easy for users to transact.