Digital transformation has found a completely different angle with the arrival of technological innovations such as AI and web3. Artificial intelligence offers unimaginable capabilities for processing data and decision making while Web3 brings the elements of decentralization and transparency. The use of AI agents in Web3 represents a convergence of the two most powerful technologies in the world right now.
Upon hearing the term ‘AI agents’, some of you may imagine AI systems behaving like James Bond or Ethan Hunt. In reality, AI agents are autonomous software programs which have the potential to transform the approaches for interacting with and working in decentralized ecosystems. Let us learn how AI agents will automate “knowledge work” in Web3.
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Understanding How AI Agents Work
You can find relevant insights on the utility of AI agents in the domain of Web3 only if you know how they work. AI agents are software programs which don’t follow a specific set of rules. On the contrary, they have the capabilities for reasoning, planning, learning and adapting to achieve the desired goals. The web3 artificial intelligence relationship will grow stronger with AI agents which are nothing like simple chatbots. An AI agent can utilize advanced AI models such as Large Language Models or LLMs for understanding complex requests, processing information, and making relevant decisions.
The best way to describe the working mechanism of AI agents is to paint them as highly autonomous digital assistants. You can break down the workflow of an AI agent into the following steps.
The first step in the working of AI agents involves collecting data from different sources, including text, numbers and real-time data. AI agents use the collected data to perceive the environment in which they have to work.
Subsequently, the agent uses their AI model, generally an LLM, to analyze the data and come up with definite conclusions. The reasoning leads the AI agent to craft a step-by-step plan for achieving its goals.
Once the agent has finalized the course of action, it will execute the plans, interact with other systems, generate content, and perform transactions, whichever required.
Most important of all, AI agents learn from their experiences and refine the way they work to improve their performance. The ability to learn and act autonomously makes AI agents different from conventional software programs.
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Establishing the Connection between AI Agents and Web3
The idea approach to discover insights on the utility of AI agents for the web3 landscape would require a clear understanding of how AI systems fit in the web3 world. Web3 or the decentralized variant of the internet, uses blockchain technology to empower users with ownership of their data. The other notable traits of Web3 include peer-to-peer interactions and censorship resistance.
You can find the answers to “What are web3 AI agents?” in the different ways for linking web3 with AI. Do you know that most of the AI applications in the existing web2 world are centralized? Big corporations own the AI models, infrastructure and data that are responsible for the working of AI applications. Therefore, you can come across issues of censorship, lack of transparency, and data privacy.
Web3 can come into the picture and decentralize intelligence by enabling AI agents to run on distributed networks. As a result, AI agents will not depend on central servers, thereby becoming more censorship-resistant and resilient.
Blockchain can support secure data management with transparency, thereby ensuring that users can control access to their data while empowering AI agents with different functionalities.
The relationship between Web 3.0 and AI agents is also evident in the creation of a completely distinctive ecosystem. Web3 tokenomics can help in creating mechanisms to serve incentives for developing, deploying, and using AI agents. It can provide the ideal foundation for a synergetic and collaborative ecosystem.
One of the most noticeable highlights underlying the importance of web3 AI agents revolves around community governance. The introduction of AI agents in the world of Web3 helps in ensuring that AI grows and evolves according to the needs of users rather than the whims of corporate giants. Web3 can bring DAOs for community governance of AI projects, thereby allowing different communities to cast votes on updates, ethical guidelines, and resource allocation.
Use Cases of Web 3.0 AI Agents for Knowledge Work
The biggest doubt on the mind of every reader right now must be about the meaning the ‘knowledge work’. The description of knowledge work focuses on thinking, problem-solving and rational analysis tasks rather than physical labor. You can discover the impact of AI agents on knowledge work in Web3 in the following areas.
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Automation of DeFi Platforms
The complexity of the DeFi landscape can be extremely challenging for a beginner to navigate. At the same time, you cannot ignore the diverse opportunities for lending, borrowing, yield farming, and trading in the DeFi ecosystem. The arrival of AI agents in crypto and DeFi will help people in navigating the different DeFi platforms and optimize their strategies. The most common use case of AI agents in the field of web3 is portfolio management as AI agents are capable of real-time trend monitoring.
AI agents can also help with identification of the most profitable liquidity and staking opportunities throughout different DeFi protocols. It can provide profitable ways to optimize yields, thereby saving time and reducing gas fees. AI agents can also enable access to arbitrage opportunities in DeFi by processing data from different decentralized exchanges. The impact of AI agents in DeFi will also focus on enhanced security as they can scan DeFi protocols continuously to identify vulnerabilities.
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New Perspective on Web3 Gaming and Metaverse
The blend of Web3 with AI agents will enhance knowledge work to provide more engaging and dynamic experiences in Web3 games and metaverse platforms. As the uses of AI agents in Web3 gain recognition, you can find better prospects for creating intelligent NPCs in Web3 games. AI agents can drive NPCs with realistic behaviors, adaptive dialogue, and evolving personalities to make web3 games more immersive. AI agents also play a crucial role in enhancing knowledge work for Web3 games and metaverse platforms by creating personalized content.
The traits of AI agents also make them useful for the web3 landscape by managing in-game economy in web3 and metaverse games. AI agents can support dynamic adjustment of in-game token rewards, resource allocation, and NFT minting rates. AI agents also improve the security of Web3 games and metaverse platforms by facilitating anti-fraud detection. For instance, AI agents can evaluate player behavior and their transaction patterns to detect suspicious actions.
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Enhancing the Functionalities of DAOs
Decentralized Autonomous Organizations or DAOs, are a crucial component of the web3 landscape as they enable decentralized governance. However, DAOs can be slow as they require votes of every member to reach at the final decisions. The use of Web3 AI agents can enhance DAO operations by streamlining the different processes involved in their working. First of all, AI agents can read and evaluate DAO proposals to summarize the important points and visualize different outcomes of the voting choices.
AI agents will also have a prominent role in treasury management of DAOs through creation of optimized asset allocation strategies. They can help with automation of investment decisions and real-time tracking of financial performance. AI agents can serve as community managers to enable easier collaboration between DAO participants. Most important of all, AI agents can take on the task of voting and proposal execution on the basis of pre-approved parameters in certain cases.
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Which Technological Advancements Promote the Web3 AI Relationship?
The technological advancements in Web3 and AI have played a crucial role in encouraging the use of AI agents for knowledge work in Web3. One of the foremost highlights that you should keep in mind to understand how AI can enhance the web3 experience is the arrival of more powerful LLMs. Continuous improvements of LLMs can introduce advanced capabilities in AI agents, thereby enabling them to generate relevant and smart responses.
The web3 artificial intelligence nexus will also grow stronger with the rising use of layer 2 solutions. AI agents can interact frequently with blockchain networks by using layer 2 solutions that don’t impose excessive transaction costs. On top of it, the introduction of new frameworks allows the development of more sophisticated AI agents.
Final Thoughts
The possibility of blending AI and Web3 will provide better opportunities to enhance knowledge work in various areas. AI agents will pave the path for a new era in web3 that focuses a lot on improvement in efficiency and user experience. For instance, AI agents in crypto can support effective portfolio management by analyzing data from different sources in real-time. On top of it, AI agents also improve security of users in Web3 by identifying suspicious patterns. The utility of AI agents will continue improving with the introduction of new features and latest technological advancements. At the same time, you must remember that integrating AI agents with Web3 comes with some challenges. Discover more information about the applications of AI agents for knowledge work in Web3 right now.
*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!
