Source URL: https://cloud.google.com/blog/products/ai-machine-learning/build-web3-ai-agents-with-google-cloud/
Source: Cloud Blog
Title: How to build Web3 AI agents with Google Cloud
Feedly Summary: For over two decades, Google has been a pioneer in AI, conducting groundwork that has shaped the industry. Concurrently, in the Web3 space, Google focuses on empowering the developer community by providing public goods resources like BigQuery blockchain datasets and testnet faucets, as well as the cloud infrastructure builders will need to bring their decentralized applications to life.
AI x Web3 Landscape
AI for Web3 compasses the practical ways AI can be applied as a tool to improve efficiency and effectiveness of Web3 companies and projects – from analytics to market research to chatbots. But one of the most powerful synergies is Web3 AI agents. These autonomous agents leverage AI’s intelligence to operate within the Web3 ecosystem, and they rely on Web3’s principles of decentralization and provenance to operate in a trustworthy manner, for use cases ranging from cross-border payments to trust and provenance.
AI agents – autonomous software systems, often powered by Large Language Models (LLMs) – are set to revolutionize Web3 interactions. They can execute complex tasks, manage DeFi portfolios, enhance gaming, analyze data, and interact with blockchains or even other agents without direct human intervention. Imagine agents, equipped with crypto wallets, engage in transactions between each other using the A2A protocol and facilitate economic activities using stablecoins, simplifying complex transactions.
Key applications of AI for Web3
Some sophisticated libraries now equip developers with the tools to build and deploy them. These libraries often come with ready-to-use “skills" or "tools" that grant agents immediate capabilities, such as executing swaps on a DEX, posting to decentralized social media, or fetching and interpreting on-chain data. A key innovation is the ability to understand natural language instructions and take action on them. For example, an agent can "swap 1 ETH for USDC on the most liquid exchange" without manual intervention. To function, these agents must be provisioned with access to essential Web3 components: RPC nodes to read and write to the blockchain, indexed datasets for efficient querying, and dedicated crypto wallets to hold and transact with digital assets.
How to build Web3 AI Agents with Google Cloud
Google Cloud provides a flexible, end-to-end suite of tools for building Web3 AI Agents, allowing you to start simple and scale to highly complex, customized solutions:
1. For rapid prototyping and no-code development: Vertex AI Agent Builder Conversational Agents allows for rapid prototyping and deployment of agents through a user-friendly interface, making it accessible even for non-technical users (refer to the Agent Builder codelab for a quick start). To facilitate this simplicity and speed, the platform provides a focused set of foundational tools. Agents can be easily augmented with standard capabilities like leveraging datastores, performing Google searches, or accessing websites and files. However, for more advanced functionalities—such as integrating crypto wallets, ensuring MCP compatibility, or implementing custom models and orchestration—custom development is the recommended path.
2. For full control and custom agent architecture: Open-source frameworks on Vertex AI For highly customized needs, developers can build their own agent architecture using open-source frameworks (Agent Development Kit, LangGraph, CrewAI) powered by state-of-the-art LLMs like Gemini (including Gemini 2.5 Pro which leads the Chatbot Arena at the time of publication) and Claude which are available through Vertex AI. A typical Web3 Agent architecture (shown below) involves a user interface, an agent runtime orchestrating tasks, an LLM for reasoning, memory for state management, and various tools/plugins (blockchain connectors, wallet managers, search, etc.) connected via adapters.
Example of a Web3 agent architecture
Some of the key features when using Agent Development Kit are as follows:
Easily define and orchestrate multiple agents across many agents and tools – For example you can use sub agents each handling part of the logic. In the crypto agent example above, one agent can find trending projects or tokens on Twitter/X, while another agent will do some research about those projects via Google Search and another agent can take actions on the user’s behalf using the crypto wallet.
Model agnostic – you can use any model from Google or other providers and change very easily
Intuitive local development for fast iteration – One can visualize agent topology and trace agent’s actions very easily. Just run the ADK agent locally and start testing by chatting with the agent.
Screenshot of ADK Dev UI used for testing and developing agents
Supports MCP and A2A (agent to agent standard) out-of-the-box: Allow your agents to communicate with other services and other agents seamlessly using standardised protocols
Deployment agnostic: Agents can be containerized and deployed on Agent Engine, Cloud Run or GKE easily. Vertex AI Agent Engine offers a managed runtime environment, where Google Cloud handles scaling, security, infrastructure management, as well as providing easy tools for evaluating and testing the agents. This abstracts away deployment and scaling complexities, letting developers focus on agent functionality.
Get started
We are always looking for Web3 companies to build with us. If this is an area you want to explore, please express your interest here.
For more details on how Web3 customers are leveraging Google Cloud, refer to this webinar on the Intersection of AI and Web3.
Start building today with tools like Agent Development Kit, Vertex AI Agent Builder and Vertex AI Agent Engine.
Thank you to Pranav Mehrotra, Web3 Strategic Pursuit Lead, for his help writing and reviewing this article.
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AI Summary and Description: Yes
Summary: The text discusses Google’s initiatives in blending AI with Web3 technologies, particularly focusing on the development of AI agents that enhance Web3 applications. It outlines the capabilities of these agents and Google’s tools like Vertex AI Agent Builder that simplify their creation, making AI more accessible in decentralized frameworks.
Detailed Description:
– **Overview of AI and Web3**: Google has been a pivotal force in AI for over two decades while simultaneously fostering the Web3 developer community by providing essential resources.
– **AI for Web3**: AI can enhance various aspects of Web3, such as:
– **Operational Efficiency**: AI’s implementation in analytics and market research can boost productivity.
– **Web3 AI Agents**:
– Autonomous agents powered by LLMs (Large Language Models) revolutionize interactions in the Web3 environment.
– They anchor their operations on Web3 principles, enabling applications in cross-border payments and transaction trust.
– These agents operate without human intervention to execute tasks, manage decentralized finance (DeFi) portfolios, enhance gaming experiences, and facilitate blockchain interactions.
– **Tools for Building AI Agents**:
1. **Vertex AI Agent Builder**:
– Provides a no-code development platform, catering to both technical and non-technical users.
– Facilitates simplicity and speed in agent deployment while allowing advanced functionalities through custom development.
2. **Open-source Frameworks**:
– Developers can create tailored agents leveraging state-of-the-art LLMs using frameworks like the Agent Development Kit.
– It allows intricate architecting of agents capable of performing complex tasks, highlighting flexibility and technical depth in agent development.
– **Key Features of the Agent Development Kit**:
– **Multiple Agent Orchestration**: Manage numerous agents for distinct tasks within the crypto space, promoting efficient operation across different logical compartments.
– **Model Agnosticism**: Users can select models from various providers and switch between them seamlessly.
– **Local Development Environment**: Developers can visualize and trace agent activity effectively, promoting iterative testing and innovation.
– **Deployment Flexibility**:
– Agents can be deployed across various environments (e.g., Cloud Run, GKE) and are built to handle scalability and security autonomously, freeing developers to concentrate on enhancing agent functionality.
– **Conclusion**: Google is keen to collaborate with Web3 companies, encouraging the use of their innovative tools for creating AI-driven decentralized applications. The discussion points towards a burgeoning intersection of AI and Web3, underlining the practical applications of AI agents that can effectively perform tasks, driving new efficiencies in the decentralized landscape.
By focusing on enhancing both productivity and creativity within Web3 frameworks, this initiative represents a significant evolution in how AI can be integrated into emerging technologies, thus offering professionals in security and compliance a window into potential operational upheaval in how digital projects and assets are managed securely and effectively.