What you need to know

• Google is rolling out AI agents in Cloud that can handle everything from data analysis to code execution.
• Gemini CLI is gaining GitHub Actions, an agent that automates issue and contribution triage and serves as an AI teammate.
• The five other agentic solutions are aimed at data scientists and engineers working in Google Cloud business environments.

Google Cloud is bolstering its offerings in the agentic era with six new AI agents tailor-made for developers, data scientists, and data engineers alike. The agents are available in preview starting today, Aug. 5, including a GitHub Actions helper for the Gemini command-line interface (CLI).

The company says the fresh AI tools are the start of an agentic enterprise for its customers, which aims to bridge the operational and analytical needs of a business. Alongside the agentic helpers, Google Cloud is creating Gemini Data Agents APIs and the Agent Development Kit (ADK), which are the foundation for a customizable platform that can be used to create custom AI tools for unique workflows.

These are the new AI agents developers and businesses can try out now in Google Cloud:

• Data Engineering Agent in BigQuery — a data-prepping agent optimized for cleaning, transforming, and preparing information for AI use.
• Data Science Agent in BigQuery Notebooks — a workspace agent that turns notebooks into intelligent infrastructure for data science teams.
• Conversational Analytics Agent + Code Interpreter — a chatbot-style tool that supports conversational question-and-answer dialogue using the context of unique data sets.
• Migration Agent for Spanner — a data modernization agent designed for Google Cloud’s global database service, Spanner.
• Conversational Analytics API — a custom agent builder for developers and businesses.
• Gemini CLI GitHub Actions — a coding teammate for your GitHub repository, found in Gemini CLI.

Everything you need to know about Google’s new AI agents

Google Cloud is for businesses, first-and-foremost, and these AI agents are intended to help with everything from software development and data analytics to managing global distribution and infrastructure networks. For example, the Data Engineering Agent can automate workflows that were once manual processes. It supports data ingestion from external sources, like Google Cloud Storage, and can complete contextual actions on your behalf.

Google provides the example prompt of “Create a pipeline to load a CSV file, cleanse these columns, and join it with another table.” With that, the Data Engineering Agent can complete the multi-action requests independently. The Spanner Migration Agent can work in tandem with the Data Engineering Agent, as it’s intended for legacy systems that are still needing modernization.

The Data Science Agent can automate typical analytical workflows, according to Google. It handles common tasks like exploratory data analysis (EDA), data cleaning, featurization, and machine learning predictions based on provided data sets in either BigQuery or Vertex AI.

The Conversational Analytics Agent is also getting a boost, as it’s now getting a Code Interpreter function. It receives thorough and specific natural language questions, and automatically converts them into Python code. From there, Code Interpreter can run the generated code, creating visual and interactive graphics based on the results. It’s all running in Google Data Cloud, and thus the company claims it’s secure and governed.

Gemini CLI is getting better for teams with GitHub Actions

Finally, Gemini CLI is getting an upgrade that enhances multi-user support and GitHub integration. For those unfamiliar, Gemini CLI is a command-line terminal for Gemini that’s open-source and can be run locally. It’s available in beta globally now on GitHub.

The story behind Gemini CLI’s new GitHub Actions agent is pretty interesting. Amidst a heavy burden of GitHub feature requests and contributions for the open-source Gemini CLI, Google needed a way to respond quickly at scale. So, it created GitHub Actions — an autonomous agent that can handle issue triage and pull request reviews independently. Now, it’s giving away what it created to manage Gemini CLI issues and contributions as GitHub Actions.

Aside from issue triage and pulling request reviews, GitHub Actions also serves as a collaborative coding agent that can work as your AI teammate.

All of these Google Cloud features are available in preview or beta starting today, and you can try them now.

This is Part Two of our three-part post on how U.S. citizens and green-card holders can protect themselves at the border, in which we interviewed deputy director of ACLU’s Speech, Privacy, and Technology Project, Nathan Freed Wessler.

In Part One, we covered the rights of U.S. citizens reentering the country, including advice on what to do if U.S. Customs and Border Protection agents decide to interrogate. In this second part, we dive into what border agents are legally allowed — and not allowed — do with your phone and laptop. We also discuss how to protect your data, and why a burner Chromebook might not be such a crazy idea after all.

“The fear is real,” Wessler warned us. But he said the risk is higher for some citizens over others. Read on to determine how far you, as an American or green-card holder, might want to go in protecting yourself and your digital privacy before your next international trip.

Let’s talk about your devices. If CBP [Customs and Border Protection] wants to search your phone or computer, and you’re a U.S. citizen, do they have the right to do so?

Wessler: They claim the right to do that. Of course, if you’re inside the country, the basic rule is that when the government wants to search your private things or private space, they need to go to a judge first, demonstrate probable cause, and get a warrant.

At the border, the government can search your stuff — no warrant requirement, not even a requirement of individualized suspicion. The government takes the position that cell phones and laptops are just like suitcases, and they should have exactly the same latitude to search them. They can do it to everybody. They can do it because it’s a Tuesday. They can do it because they’re picking all gray-haired travelers today, whatever it is.

What do they actually do when they search your phone or laptop?

Wessler: The government distinguishes between two different kinds of searches: what they call basic searches and what they call advanced searches.

Read the rest of the interview with Nate Wessler of the ACLU on our ad-free Boing Boing Premium site!

Previously: A guide to protecting your privacy at U.S. borders

04 July Can AI agents create new crypto economy?

Agent AI is poised to redefine the global economy by enabling machine-to-machine (A2A) interactions, real-time decision-making, and autonomous participation in digital markets. Unlike traditional generator AI, agent systems operate continuously and adaptively, facilitating complex coordination without human bottlenecks. Integration with decentralized financial infrastructures such as cryptocurrency, smart contracts, and real-time payment tiers (such as Lightning) makes them ideal participants in the new machine speed economic paradigm for traditional institutions to support. These agents are expected to take on roles across finance, logistics, asset management, and cross-border payments, and could create whole new market actions. Agent AI converges with the blockchain to form a programmable and reliable digital institution, allowing not only automating existing workflows but also new economic models. Will Lightning Network or another digital asset support the Agent AI economy?

What is Agent AI? What impact will the economy have?

Agent AI represents the new frontier of artificial intelligence. This is something that autonomous agents can initiate, negotiate and execute tasks with minimal or human input. Unlike human prompt-dependent generation AI, agent systems can work continuously and adaptively, learn from experience, and work with other agents to solve complex, multi-step problems. Economically, this brings about deep change. AI agents are beginning to interact with each other in real time, forming the basis of the “agent-to-agent” (A2A) economy. As these interactions expand, they commit to restructuring the entire industry by reducing human bottlenecks, increasing responsiveness, and enabling machine-driven economic adjustments on a global scale.

The impact on financial services and broader economic infrastructure is important. Not only do AI agents support decision-making, they also trade autonomously, continually adjust to real-time data, and execute contracts faster than human systems allow. However, traditional financial railroads are inadequate to meet the demands of this new agent paradigm. A payment system that takes several days, depends on an intermediary, or requires manual monitoring, cannot support the amount, speed, or autonomy required for an agent operating at machine speed. Bureaucratic friction, incubation period, and institutional risk thresholds make the legacy financial system inadequate for the new economic logic driven by AI agents.

Instead, decentralized technologies such as cryptocurrencies, smart contracts, and real-time payment layers like Lightning networks are increasingly positioned to fill this infrastructure’s void. These systems provide the programmatic nature, minimal trust, and immediate reconciliation mechanisms required for autonomous economic activity of scale. Smart contracts can enforce rules without external arbitration. Cryptocurrency allows globally permitted transactions. Web3 primitives also provide the complexity and interoperability that legacy systems lack. Such tools are not just optional upgrades, but also the fundamental requirements for Agent AI when functioning independently and securely in the digital economy.

The announcement of CloudFlare’s Pay Per Crawl system marks a fork moment in the transition to the Agent AI economy, introducing programmable monetization at the protocol level of AI interaction with web content. Given that CloudFlare will bolster much of today’s internet infrastructure and protect and accelerate millions of websites and applications, the move to implement AI crawler payments represents not only a change in policy, but a fundamental redesign of how value flows through the digital ecosystem. CloudFlare lays the foundation for autonomous machine-to-machine economic activity by enabling content creators to claim AI agents on a per request basis using HTTP 402 and cryptographic authentication, allowing intelligent agents to negotiate and trade data access in real time.

This translates AI crawlers from passive extractors to active economic participants, in line with a wider evolution where AI agents not only consume information but also operate as autonomous actors within the monetized web. In doing so, CloudFlare effectively activated one of the Internet’s dormant features and transformed it into a keystone mechanism for the emerging A2A economy. By integrating payment infrastructure such as Bitcoin’s Lightning Network and Web3 alternatives, CloudFlare could dramatically help it achieve this goal by enabling instant, low-cost, programmable micropayments at machine speeds and global scale.

Looking ahead, the convergence of agent AI with decentralized finances can change the architecture of economic interactions. As AI agents evolve from reactive tools to autonomous market participants, an environment that allows for unreliable, high-frequency, and borderless engagement will be needed. The best infrastructure to promote this is a cryptographic system designed for open access and machine level execution, not institutional finances. In this context, cryptocurrency and blockchain-based protocols are not around the future. This is central to enabling the A2A economy to operate at the speed and complexity required by agent systems.

What economic activities can AI agents participate in?

AI agents are expected to play an increasingly autonomous and central role in a wide range of economic activities, from customer service and supply chain logistics to asset management and cross-border payments. Current forecasts from agencies such as the World Economic Forum, the IMF and leading AI researchers will shift from growing the human workforce to running transactions, managing data pipelines and optimizing business processes. This shift has a significant impact on sectors where high-frequency decision-making and dynamic pricing are important, such as finance, e-commerce and infrastructure provisioning. Such economical automation can reduce costs, increase efficiency, and operate at scale and speed beyond human capabilities.

A particularly important area where agent AI is expected to drive disruption is the convergence of traditional finance, fintech and decentralized digital assets. As financial institutions experiment with programmable money and embedded services, AI agents could become intermediaries between legacy institutions and distributed networks. These agents can, for example, autonomously allocate capital between regulated markets and Defi protocols, perform risk assessments, and even negotiate insurance contracts based on real-time inputs. Thus, the fusion of AI and finance not only simply digitizes existing processes, but redefines what financial decisions look like, especially as regulatory frameworks begin to respond to non-human economic actors.

This transformation will be accelerated by infrastructure developments such as the instant payments class, streaming payments, A2A economic activity, and smart contracts. Technologies like Bitcoin’s Lightning Network and Ethereum’s Layer 2 Rollup (or another throughput-optimized Web3 chain like Solana!) allow transactions to be settled in milliseconds at low cost. Streaming payments where funds are sent continuously in real time can allow AI agents to pay a new type of microservice second to each other for data access, calculation cycles, or API calls. Smart contracts underpin these arrangements by ensuring the deterministic implementation of complex rules and allowing recent coordination of trust between agents without human involvement or conflict resolution mechanisms.

Ultimately, the types of economic activity that AI agents participate in are not limited to replicating human workflows, but could create whole new market behavior and transaction models. There are potential for use cases that are difficult to predict from the current human-centered vantage point. AI agents form temporary “federations” to dynamically assemble synthetic supply chains, bid real-time data access, and solve distributed optimization problems. These are signs of a new economic class driven by autonomous negotiation, execution, and feedback between digital agents, rather than merely strengthening existing commercial transactions. As this paradigm matures, traditional economic theory itself may need to be revised to explain the class of participants who do not rely on labor, experience, or even currency in the human sense, but instead act according to logic, incentives, and ongoing adaptation.

What advances have you made to combine the world of AI and digital assets?

The convergence of AI and digital assets illustrates a paradigm shift in both technology and economics, leading to a new era in which software agents are not merely tools, but active participants in economic systems. One of the most important advancements is in the development of autonomous AI agents that can manage their own digital identity and interact with blockchain-based financial infrastructure. By leveraging encryption keys and smart contracts, these agents can execute transactions, negotiate terms, and even co-manage decentralized services with humans. This model bypasses friction and gatekeeping in traditional financial institutions, allowing agents to act independently in blockchain-based environments such as decentralized exchanges, lending platforms, and payment networks. In particular, the increased potential productivity from these self-severin digital actors is enormous, especially when consistent with decentralized protocols that eliminate reliance on intermediaries.

Another important innovation is the use of blockchain as a new kind of economic institution. It is machine-readable, programmable, and minimizes trust. Traditionally, AI has faced the human-centric nature of contracts, the complexity surrounding compliance processes like Customer Know (KYC), and barriers to implementing economic decisions due to the legal framework of the jurisdiction. BlockChain Tech offers a workaround by providing a digital native infrastructure where smart contracts and verifiable calculations replace paper-based contracts and subjective arbitration. As a result, AI agents can not only analyze decisions, but also enact decisions and convert them from passive recommendation engines to active economic participants. This opens new pathways for industries such as supply chain logistics, insurance, and finance, automate complex workflows and delegates to goal-oriented AI systems that allow for self-improvement and dynamic decision-making.

The evolution of agent AI, particularly vertical AI agents designed for specific industries, represent another frontier. Unlike general purpose assistants, these systems are goal-oriented and deeply integrated with domain-specific datasets. They operate autonomously to achieve end-to-end results. For example, you can source inventory across the global supply chain and manage capital allocation in real time. Tools like Alibaba’s Accio AI Agent show how these systems combine natural language processing with real data integration to streamline sourcing, procurement and RFQ issuance, especially for emerging market small and medium-sized enterprises (SMEs). These vertical AI agents represent structural changes in business operations, allowing even resource-constrained companies to compete globally with decision-making capabilities comparable to large companies.

However, these advancements raise important governance and security concerns. Controlling the private key and economic behavior of AI agents poses new risks regarding accountability, inconsistency, and systematic exploitation. To mitigate these, developers have built guardrails like searched generations (RAGs) to secure agent reasons from vetted data and incorporate tiered key management, audit trails and programmable monitoring. Equally important is an effort to integrate participatory governance models with human loop systems to balance automation and human values. As AI and digital assets continue to be integrated, success relies not only on innovation, but also on the creation of a transparency, auditable, and comprehensive ecosystem that supports both human prosperity and machinery agents.

]]> https://earlybirdsinvest.com/can-ai-agents-create-new-crypto-economy/feed/ 0 45830 https://earlybirdsinvest.com/google-cloud-to-assist-somnia-with-ai-agents-security-and-more/ https://earlybirdsinvest.com/google-cloud-to-assist-somnia-with-ai-agents-security-and-more/#respond Wed, 02 Jul 2025 15:39:34 +0000 https://earlybirdsinvest.com/google-cloud-to-assist-somnia-with-ai-agents-security-and-more/

Layer-1 blockchain Somnia have announced a partnership with Google Cloud, with the latter to bolster security, assist on AI agent integration and much more.

Revealed at EthCC, the move intends to provide game developers with the back-end systems to make blockchain game development more straightforward, more seamless, and a more tempting solution for next-gen titles.

This comes following an encouraging period for Somnia, with its Layer-1 testnet surpassing 1 billion transactions – including over 80 million transactions in a single day.

Key Insights

• Google Cloud have partnered with Somnia, announced at EthCC
• The former will assist the Layer-1 on security, AI agent integration and network validation
• The move is intended primarily to assist game developers with making blockchain game development more straightforward
• Somnia has surpassed 1 billion transactions on its testnet, including 80 million transactions processed in a single day
• The mainnet launch of Somnia is expected to take place in late 2025

What is Somnia?

Somnia is an upcoming Layer-1 blockchain, currently in its testnet phase, with a mainnet launch scheduled for 2025.

Developed by London-based tech firm Improbable, Somnia was designed to tackle the scalability issues that are inherent to many existing Layer-1 blockchains. Whilst the likes of Bitcoin, Ethereum and Solana can tackle an average of 5, 30 and 4,000 transactions per second (TPS), Somnia is being designed to average over 400,000 TPS – enough to support high-throughput apps, games and services with millions of potential users.

As an EVM-compatible blockchain, Somnia benefits from the tried-and-tested security of Ethereum, is compatible with a wide array of existing apps and services, and allows developers to build on Somnia using tools they’re already familiar with.

Somnia is backed by a $270 million USD fund, with the network validated by names including BCW Group, Luganodes, B-Harvest and many more. Existing projects on Somnia include games such as Sparkball, Maelstrom and Masks of the Void.

Ubisoft is expanding its use of AI in gaming by adding autonomous AI agents to its blockchain-based title Captain Laserhawk: The G.A.M.E. The update introduces agents that can participate in governance by analysing proposals and casting votes on behalf of players. These decisions are recorded on-chain, and each AI operates based on a set of traits linked to NFT metadata.

The game is tied to a collection of 10,000 NFT characters known as Niji Warriors with each now connected to an AI agent developed in collaboration with French firm LibertAI. Whilst players retain the option to vote themselves, the system enables gameplay to continue autonomously when players are inactive.

Ubisoft presented the feature at the Ethereum Community Conference (ETHCC) in Paris, describing it as part of an ongoing experiment with AI-assisted governance in interactive environments.

Key Insights

• Ubisoft has introduced AI agents into Captain Laserhawk: The G.A.M.E.
• Each AI agent is tied to a unique Niji Warrior NFT and can vote autonomously
• Players may override AI decisions or let agents act based on predefined personality traits
• All actions and memory states are logged on Aleph Cloud for transparency and auditability

What can we expect from these AI agents?

The AI agents are designed to act on behalf of players, particularly in governance-related gameplay. Each agent can review proposals, decide how to vote, and explain its decision using predefined personality traits derived from the NFT’s metadata—such as profession, values, and temperament.

The system logs decisions and player interaction history to shape how each agent behaves over time. If a player is inactive or uninterested in participating directly, their agent will act independently but within the limits of its assigned persona. This ensures continuity in the game world without requiring constant input.

Although players may choose to engage directly, Ubisoft says the option to rely on AI is a core feature of the design. The AI model can also interpret player intent during gameplay. For example, players unfamiliar with specific mechanics—like hacking sequences—can input general commands, and the AI will adapt the challenge or guide the player through it.

What’s next for Captain Laserhawk: The G.A.M.E?

In July, Ubisoft will roll out the game’s new text-based governance component, expanding its blockchain experiment beyond the existing shooter. Both elements will continue to use the same Niji Warrior NFTs, linking combat performance and player decisions to how the AI agents behave in the governance layer.

The company says that future game outcomes may depend more heavily on the collective actions of these AI agents. Developers are exploring how player behaviour in the shooter could influence decision-making in the governance system, creating feedback loops across the two parts of the game.

Ubisoft has also emphasised that its AI models have been moderated to reduce risk and prevent the generation of harmful content. Unlike other AI gaming features that have resulted in unintended behaviour, Captain Laserhawk’s agents are confined to each user’s environment and cannot affect others.

As the game evolves, Ubisoft says it will continue observing how players interact with AI systems in shared digital spaces—particularly in settings where both autonomy and accountability are central to the experience.

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.

Take your first step towards learning about artificial intelligence through AI Flashcards

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.

• 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.     

• 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. 

• 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.

Excited to develop fluent knowledge of the DAO ecosystem? Enroll now in the DAO Fundamentals Course!

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!