Google Unveils Agent Development Kit (ADK) to Streamline AI Agent Creation and Deployment

Google's new Agent Development Kit lets enterprises rapidly prototype and deploy AI agents without recoding

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In the past year, enterprises saw an explosion of platforms where they can build AI agents, preferably with as little code as possible. With the growth of agentic ecosystems from organizations, it's not a surprise that large model providers are starting to develop all-in-one platforms for creating agents and managing these. For this reason, Google announced today that it has expanded its agentic offerings, competing against many other agent-building platforms. However, Google said its new Agent Development Kit (ADK) and other additional capabilities also offer control over how agents behave. The company said the ADK simplifies the creation of multi-agent systems on Gemini models. Google claims users can "build an AI agent in under 100 lines of intuitive code" with ADK. The platform also supports the Model Context Protocol (MCP), the data connection protocol developed by Anthropic that helps standardize data movement between agents. Google said ADK will help organizations: ADK is optimized for Gemini models, though Vertex AI allows access to models from Anthropic, Meta, Mistral, AI21 Labs, CAMB.AI and Qodo. Google said developers can use ADK to ground agent and application responses to different data connectors. More agentic support Google also introduced Agent Engine, a managed runtime dashboard parallel to ADK with enterprise-grade controls. Google told reporters in a briefing that Agent Engine allows organizations to go from concept to training to eventual production. It handles "agent context, infrastructure management, scaling complexities, security, evaluation and monitoring." Agent Engine integrates with ADK but can be deployed on other frameworks like LangGraph or CrewAI. With short -- and long-term memory support, users can keep context for agents. They can customize how much or how little information from past conversations or sessions the agents can pull. Agent Engine also lets enterprises evaluate the agents' behavior and reliance during real-time usage. Companies wanting more help building agents can access Google's new Agent Garden. Agent Garden, like a model garden, is a library of pre-built agents and tools that users can use to model their agents. Google's version, through ADK and Vertex AI, would bring additional configurations for enterprises. These include: Agent platform competition heats up Enterprises have previously been able to build agents with Google's AI services. Still, the launch of ADK and its other agentic AI offerings puts Google more in competition with other agent providers. Technology companies are increasingly offering an all-in-one agent-building platform. Google has to prove that its one-stop agent creation platform, optimized for Gemini models and Vertex AI, is a better choice. OpenAI released Agents SDK in March, which lets people build agents with open-source tools, including non-OpenAI models. Agents SDK also offers configurable enterprise security and guardrails. Amazon's Agents on Bedrock, launched in 2023, also allows organizations to build agents in one place and Bedrock was updated to provide orchestration capabilities last year. Newcomer Emergence AI released an agent builder platform that lets people make any AI agent they need on the fly. Human users specify the task they need to finish, and AI models create the agents to accomplish it.

Google rolls out updates for building multi-agent AI ecosystems - SiliconANGLE

Google rolls out updates for building multi-agent AI ecosystems Google LLC is pulling out all the stops to give developers all the tools they need to bring the next era of generative artificial intelligence agents into reality, including access to powerful AI models, development kits and platforms to build and deploy AI agent-driven applications. Today during the Google Cloud Next 2025 event in Las Vegas, the search giant said the next phase of the agentic AI trend will involve multi-agent systems, where multiple AI agents will work together - even if they are built on different frameworks or operate from different providers. AI agents are intelligent systems that can act on a user's behalf using an AI model to reason, plan and execute multistep tasks with minimal or no human interaction. By combining multiple agents, they can complete even more complex tasks that cross business intentions and relationships. To enable that, Google announced that Vertex AI will receive several tools and enhancements to provide developers and AI engineers to build agents for the multi-agent future. They include an open-source Agent Developer Kit for building agents, an Agent Garden filled with example agents, and an Agent Engine for deploying custom agents into production. Vertex AI is Google's fully managed platform for developers to train and deploy AI models and applications. It also contains numerous tools for customization and training of large language models, which are fundamental for powering AI agents. With the addition of these agentic AI tools, developers will be able to build and deploy multi-agent ecosystems. The Agent Development Kit is an open-source framework aimed at simplifying AI agent development designed for multi-agent systems while allowing developers precise control over their behavior. It's currently available in the Python programming language, with more languages coming later in the year. According to Google an AI agent can be built and deployed with under 100 lines of code. Developers can choose to develop their own agent from scratch or jumpstart development with an example agent chosen from the Agent Garden. The Garden is a collection of ready-to-use sample agent patterns and components accessible to the ADK chosen to help developers build their applications quickly. AI engineers can also choose what model they want to run under the hood, from Google's flagship Gemini or any model within the Model Garden. Google said Vertex AI provides access to over 200 models from third-party providers, including Anthropic PBC, Meta Platforms Inc., Mistral AI, AI21 Labs and Qodo. "Using Agent Development Kit, Revionics is building a multi-agent system to help retailers set prices based on their business logic -- such as staying competitive while maintaining margins -- and accurately forecasting the impact of price changes," said Aakriti Bhargava, vice president of product engineering and AI at Revionics. Revionics used the ADK to streamline the process of building multi-agent transfer and planning for data retrieval and tooling to automate entire pricing workflows, Bhargava added. Once developers have built agents, they need to deploy them. Agent Engine provides a fully managed runtime for deploying agents in production. Agent Engine integrates with ADK, or any other framework such as LangGraph, Crew.ai and others, to make it easy to go from development to deployment. It includes evaluation tools from Vertex AI to improve agent performance or fine-tune underlying models to refine agent behavior based on real-world usage. Google said in the coming months it intends to expand Agent Engine with advanced tools and testing capabilities. Agents will be able to execute code and use applications like a human user. The company said it also intends to release a simulation environment to allow developers to rigorously test agents to ensure reliability in real-world situations. To network agents across the enterprise ecosystem, Google introduced the Agent2Agent protocol, which enables agents to communicate with each other irrespective of the framework or vendor they are built on. Using A2A, agents can publish their capabilities and negotiate how they will interact before establishing secure communication to complete tasks. To build the A2A open communication standard, Google partnered with over 50 industry leaders including Box, Deloitte, Elastic, Salesforce, ServiceNow, UiPath, UKG and Weights & Biases. Google Agentspace is a centralized tool for providing enterprise employees with knowledge and expertise powered by generative AI agents that can take actions on their behalf. Launched in December, the product uses Google foundation models, powerful agents and enterprise data to allow employees to discover information, discover actionable insights and execute tasks. Today, the company said it is expanding Agentspace so that enterprise employees can access it directly from the search box in the Chrome Enterprise browser. Workers can now also discover new agents from the Agent Gallery and create new ones using the no-code Agent Designer. "Imagine being able to find any piece of information within the organization - whether that's text, images, websites, audio, and video - with the ease and power of Google-quality search," said Raj Pai, vice president of product management for cloud AI at Google. "That's what we're bringing to enterprises with Google's AI-powered multimodal search capabilities in Agentspace." Agent Gallery is generally available to allow listed users who can now choose available agents from across the enterprise, including from Google, built by internal teams and partners. At launch, the Gallery includes two new Google-built expert agents joining the previously available NotebookLM for Enterprise. Deep Research agent will explore complex topics on behalf of users similar to the Deep Research capability of the Gemini LLM and synthesize information from internal and external sources into an easy-to-read report. An Idea Generation agent will brainstorm and develop novel ideas in any area, then help evaluate them to find the best solution "via a competitive system inspired by the scientific method," the company said. Agent Designer is in preview, which provides a no-code interface for connecting to enterprise data sources to automate everyday knowledge work tasks. Even users with limited technical expertise will be able to build AI agents personalized for their day-to-day practices and needs. Beyond expert agents and building agents, Agentspace also supports the Agent2Agent protocol, which will allow its agentic platform to communicate with external agents to complete tasks on the user's behalf.

Google Agent SDK Launches with Multi-Agent Architecture and Cloud Integration

Google has unveiled its Agent SDK, a comprehensive toolkit aimed at simplifying the creation and deployment of AI agents. This strategic release positions Google as a key player in the competitive AI development landscape, alongside established names like OpenAI, LangChain, and Llama Index. With features such as cloud deployment, multi-agent architecture, and seamless integration with existing tools, the Agent SDK addresses critical challenges in building intelligent systems. Although still in its early stages, this toolkit reflects Google's vision for the future of AI development, offering developers a powerful resource to create scalable and efficient AI solutions. Designed with developers in mind, the Agent SDK brings together the power of cloud deployment, multi-agent collaboration, and integration with popular tools like LangChain and OpenAPI. Whether you're tackling complex workflows or simply looking for a smoother way to deploy AI agents, this toolkit promises to simplify the process while addressing some of the frustrations that have plagued existing frameworks. While it's still in its early days, the SDK offers a glimpse into a future where creating intelligent systems is not only more accessible but also more powerful. The Agent SDK is designed with cloud deployment as a core feature, allowing developers to design, test, and deploy AI agents directly within cloud environments. This approach ensures scalability, reliability, and global accessibility, making it particularly well-suited for enterprise-level applications. By using Google's robust cloud infrastructure, developers can focus on building intelligent agents without the complexities of managing on-premise systems. This cloud-first strategy aligns with the growing demand for scalable, remote AI solutions, offering businesses the flexibility to adapt to dynamic workloads and user demands. One of the standout features of the Agent SDK is its support for multi-agent architecture, which allows multiple AI agents to interact and collaborate within a unified system. This capability is especially valuable for automating complex workflows that require coordination between different agents. For example, a customer support agent could work in tandem with a data analysis agent to provide real-time insights while addressing customer queries. By allowing coordinated interactions between agents, the SDK enables organizations to tackle sophisticated tasks with greater efficiency and precision, paving the way for more advanced AI-driven solutions. Advance your skills in Google Agent SDK by reading more of our detailed content. The Agent SDK is designed to integrate effortlessly with popular tools and frameworks, including LangChain, OpenAPI, and Llama Index. This compatibility allows developers to enhance their existing technology stacks while incorporating new functionalities. Built-in features such as function calling and authentication systems further streamline the development process. For instance, developers can easily connect their agents to external APIs or secure systems without extensive custom coding, saving both time and effort. This seamless integration ensures that the SDK can be adopted across a wide range of industries and use cases, from customer service to data analytics. The Agent SDK offers support for a variety of large language models (LLMs), including Google's proprietary Gemini models and third-party options like OpenAI's GPT and Anthropic's Claude. This flexibility allows developers to select the model that best aligns with their specific needs and objectives. Whether building a conversational agent, a content generation tool, or a data-driven decision-making system, the SDK's LLM integration ensures high-quality language understanding and generation, enhancing the overall performance and versatility of AI agents. The SDK incorporates an event-driven architecture, allowing AI agents to respond dynamically to real-time triggers and events. This design is particularly beneficial for applications that require immediate reactions, such as monitoring systems, automated trading platforms, or real-time customer interactions. By adopting an event-driven approach, developers can create agents that are both responsive and efficient, making sure optimal performance in time-sensitive scenarios. This capability highlights the SDK's potential to support a wide range of real-world applications where responsiveness is critical. A key advantage of the Agent SDK is its support for Google's Gemini models, which offer advanced customization options. These models can be fine-tuned to adapt to specific tasks and domains, providing developers with the flexibility to optimize their agents for specialized use cases. For example, a Gemini model could be customized to understand industry-specific terminology, making it particularly useful in fields such as healthcare, finance, or supply chain management. This level of customization ensures that AI agents can deliver more accurate and relevant results, enhancing their value in specialized applications. Google has placed significant emphasis on agent-to-agent communication, a feature that is expected to evolve further in future updates. This framework enables agents to share information and collaborate effectively, facilitating the development of sophisticated multi-agent systems. For instance, in a logistics scenario, one agent could manage inventory while another oversees transportation, with both agents exchanging data seamlessly to optimize operations. This capability underscores the SDK's potential to support complex, interconnected systems, driving innovation in industries that rely on coordinated workflows. Despite its promising features, the Agent SDK is still in its early stages and faces several challenges. Current limitations include sparse documentation, a limited number of sample agents, and support restricted to the Python programming language. These factors may hinder adoption among developers who rely on other programming languages or require more comprehensive resources. However, these challenges are likely to diminish as Google continues to refine the platform, expand its capabilities, and provide more robust support for developers. Looking ahead, Google plans to introduce additional features to the Agent SDK, including more advanced agent-to-agent communication frameworks and support for a broader range of programming languages. These updates aim to make the SDK a more versatile and powerful tool for AI development. By addressing current limitations and building on its strengths, Google is positioning the Agent SDK as a leading solution in the competitive AI framework landscape. The planned enhancements reflect Google's commitment to driving innovation and supporting developers in creating innovative AI systems. Google's Agent SDK represents a significant step forward in the field of AI agent development. With its focus on cloud deployment, multi-agent architecture, and seamless integration, the SDK provides a robust foundation for building intelligent, scalable systems. Its support for Gemini models, event-driven design, and LLM flexibility further enhances its appeal, offering developers the tools they need to create advanced AI solutions tailored to diverse use cases. While still in its early stages, the Agent SDK's potential is evident, and as Google continues to refine and expand the platform, it is poised to play a pivotal role in shaping the future of AI development.

Google Agent Development Kit (ADK) Introduced at Google Cloud NEXT 2025

Google has introduced the Agent Development Kit (ADK) at Google Cloud NEXT 2025, an open source Python SDK designed to streamline the creation of advanced AI agents. By using Google's extensive expertise in artificial intelligence, ADK aims to reduce the complexity of building AI systems while granting you greater control over their design and deployment. Whether you are developing multimodal, multiagent systems or deploying AI solutions across diverse environments, ADK offers a robust and adaptable framework tailored to meet your needs. Its open source nature ensures accessibility, making it a valuable tool for developers at all levels of expertise. With ADK, Google has distilled years of AI expertise into an open source Python SDK that's as versatile as it is powerful. Whether you're working on multimodal, multiagent systems or deploying solutions across diverse environments, ADK offers a model-agnostic, deployment-agnostic framework to streamline your workflow. It's packed with tools to help you test, debug, and refine your agents with ease, all while making sure compatibility with your existing systems. If you've ever wished for a smoother, more intuitive way to bring your AI ideas to life, you're in the right place. Let's dive into how ADK is reshaping the way developers approach AI agent creation. AI development often involves navigating intricate workflows, integrating diverse tools, and making sure scalability for real-world applications. ADK addresses these challenges by providing a streamlined approach to creating production-ready agents. Its primary objective is to reduce development friction while maintaining flexibility and scalability. By offering a model-agnostic and deployment-agnostic foundation, ADK enables you to focus on innovation without being restricted to specific tools, platforms, or environments. This adaptability ensures that your AI solutions remain future-proof and versatile, regardless of evolving project requirements. ADK is built on three foundational principles: compatibility, adaptability, and interoperability. These principles make it a versatile and powerful tool for developers seeking to create sophisticated AI agents. ADK includes a comprehensive suite of tools designed to enhance the development process and improve efficiency. These tools provide the functionality needed to build, test, and refine AI agents effectively, making sure that your projects are both robust and scalable. ADK is designed with familiar software engineering principles, making it accessible to developers with varying levels of experience. Its clear agent definitions and workflows simplify the process of creating multiagent systems, while its modular architecture supports rapid iteration and scalability. By adhering to established engineering practices, ADK ensures that your projects remain maintainable and adaptable as they grow in complexity. This design philosophy not only accelerates development but also fosters collaboration among team members, allowing efficient project management. To support developers, ADK offers a wealth of resources, including sample agents and detailed documentation available on GitHub. These resources provide step-by-step guidance, from initial setup to advanced use cases, allowing you to quickly harness the full potential of the toolkit. Whether you are a beginner exploring AI development or an experienced developer seeking to optimize workflows, these materials ensure a smooth onboarding experience. The availability of practical examples and best practices further enhances your ability to create sophisticated AI agents with confidence. ADK is particularly well-suited for building multimodal, interactive agents capable of handling complex tasks such as trip planning, customer support, and more. Its flexibility and scalability make it an ideal choice for developers aiming to create AI solutions that can adapt to diverse use cases. While the current release focuses on Python, Google has announced plans to expand ADK's capabilities with Java support in the near future. This planned expansion will make the toolkit even more accessible to a broader range of developers, fostering innovation across different programming ecosystems. The ongoing development of ADK underscores Google's commitment to empowering developers with innovative tools for AI agent creation.