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DeeperML - #aiinteroperability
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Google is aggressively advancing AI agent interoperability with its new Agent2Agent (A2A) protocol and development kit. Unveiled at Google Cloud Next '25, the A2A protocol aims to standardize how AI agents communicate, collaborate, and discover each other across different platforms and tasks. This initiative is designed to streamline the exchange of tasks, streaming updates, and sharing of artifacts, fostering a more connected and efficient AI ecosystem. The A2A protocol complements existing efforts by providing a common language for agents, enabling them to seamlessly integrate and normalize various frameworks like LangChain, AutoGen, and Pydantic.
The Agent2Agent protocol introduces the concept of an "Agent Card" (agent.json), which describes an agent's capabilities and how to reach it. Agents communicate through structured messages, indicating task states such as working, input-required, or completed. By establishing this open standard, Google, along with partners like SAP, seeks to enable agents from different vendors to interact, share context, and collaborate effectively. This move represents a significant step beyond simple API integrations, laying the groundwork for interoperability and automation across traditionally disconnected systems. The development of A2A aligns with Google's broader strategy to solidify its position in the competitive AI landscape, challenging rivals like Microsoft and Amazon. Google is not only introducing new AI chips, such as the Ironwood TPUs, but also updating its Vertex AI platform with Gemini 2.5 models and releasing an agent development kit. This comprehensive approach aims to empower businesses to turn AI potential into real-world impact by facilitating open agent collaboration, model choice, and multimodal intelligence. The collaboration with SAP to enable AI agents to securely interact and collaborate across platforms through A2A exemplifies this commitment to enterprise-ready AI that is open, flexible, and deeply grounded in business context.
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Google Cloud Next '25 saw a major push into agentic AI with the unveiling of several key technologies and initiatives aimed at fostering the development and interoperability of AI agents. Google announced the Agent Development Kit (ADK), an open-source framework designed to simplify the creation and management of AI agents. The ADK, written in Python, allows developers to build both simple and complex multi-agent systems. Complementing the ADK is Agent Garden, a collection of pre-built agent patterns and components to accelerate development. Additionally, Google introduced Agent Engine, a fully managed runtime in Vertex AI, enabling secure and reliable deployment of custom agents at a global scale.
Google is also addressing the challenge of AI agent interoperability with the introduction of the Agent2Agent (A2A) protocol. A2A is an open standard intended to provide a common language for AI agents to communicate, regardless of the frameworks or vendors used to build them. This protocol allows agents to collaborate and share information securely, streamlining workflows and reducing integration costs. The A2A initiative has garnered support from over 50 industry leaders, including Atlassian, Box, Cohere, Intuit, and Salesforce, signaling a collaborative effort to advance multi-agent systems. These advancements are integrated within Vertex AI, Google's comprehensive platform for managing models, data, and agents. Enhancements to Vertex AI include supporting Model Context Protocol (MCP) to ensure secure data connections for agents. In addition to software advancements, Google unveiled its seventh-generation Tensor Processing Unit (TPU), named Ironwood, designed to optimize AI inferencing. Ironwood offers significantly increased compute capacity and high-bandwidth memory, further empowering AI applications within the Google Cloud ecosystem.
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