Celebrating the first anniversary of the Agent-to-Agent (A2A) protocol, this blog post highlights how the framework enables autonomous AI agents to securely collaborate and hand off tasks without the rigidity of traditional APIs. By delegating complex workflows to specialized peer agents, A2A prevents context pollution, ensures data privacy, and simplifies application design through modularity. To demonstrate this ecosystem in action, the post spotlights FoldRun—an agentic interface for life sciences that orchestrates complex protein structure predictions—alongside diverse A2A use cases spanning commerce, data streaming, DevOps, and telecommunications.

Celebrating the first anniversary of the Agent-to-Agent (A2A) protocol, this blog post highlights how the framework enables autonomous AI agents to securely collaborate and hand off tasks without the rigidity of traditional APIs. By delegating complex workflows to specialized peer agents, A2A prevents context pollution, ensures data privacy, and simplifies application design through modularity. To demonstrate this ecosystem in action, the post spotlights FoldRun—an agentic interface for life sciences that orchestrates complex protein structure predictions—alongside diverse A2A use cases spanning commerce, data streaming, DevOps, and telecommunications.

The Google Tensor ML SDK is graduating to its Beta phase, allowing developers to build and deploy high-performance machine learning models directly onto the TPU of Google Pixel 10 devices. By integrating with LiteRT, Google's edge deployment framework, the SDK provides a unified workflow for developers to convert, compile, and run PyTorch or TFLite models with robust fallback options. Additionally, a new model garden offers over 100 classic and generative AI models, including Gemma 3, enabling low-latency, private features like speech recognition, computer vision, and text generation.

The Google AI Edge Gallery app has expanded its on-device AI capabilities by introducing experimental support for the open-source Model Context Protocol (MCP) on Android, allowing Gemma 4 to coordinate complex tasks across external data sources like Google Workspace and Google Maps. To enable more proactive and persistent user interactions, the update adds a "Schedule Notification" skill for automating routines and a persistent chat history feature that restores long session contexts nearly instantly. Driven by an open-source toolkit, the platform encourages community developers to build and share custom utility-focused workflows, prompt configurations, and tool integrations via its GitHub repository.

Google AI Edge’s LiteRT-LM provides a production-proven, highly optimized infrastructure for running Gemma 4 across cross-platform mobile and edge environments. It actively unlocks the model's native multimodal and agentic features on-device by utilizing memory-efficient dynamic loading, Multi-Token Prediction for up to a 2.2x speedup, and advanced orchestration tools like Thinking Mode and Constrained Decoding. Furthermore, the engine is rapidly expanding its integration surfaces beyond Android, introducing new native Swift APIs for Apple ecosystems and WebGPU-accelerated JavaScript APIs for high-performance, serverless browser inference.

Google announced the transition from assistive AI to independent agents, highlighting the launch of the Gemini 3.5 series and major updates to its Antigravity agent-first development platform. For mobile developers, the post introduces new Android CLI tools, the Android Bench evaluation leaderboard, and an automated Migration agent designed to rapidly convert various frameworks into native Kotlin code. Web development is also being transformed through Chrome DevTools for agents, the HTML-in-Canvas API, and the proposal of WebMCP, an open web standard that enables browser-based AI agents to execute complex tasks.

The Google Cloud and NVIDIA developer community is celebrating its first anniversary with 100,000 members and a renewed focus on providing builders with advanced AI infrastructure and resources. To accelerate development, the community offers curated learning pathways for mastering LLM optimization, GPU-accelerated data analytics, and monthly expert-led webinars. Moving into its second year, the initiative will expand to include hands-on labs, engineering events, and specialized content focused on the growth of agentic AI.

Google is unifying its AI terminal tools by transitioning the community-focused Gemini CLI into Antigravity CLI, a new agent-first platform built for complex, multi-agent workflows. This new Go-based tool offers faster execution, asynchronous processing, and a unified architecture that syncs with the Antigravity 2.0 desktop application. While enterprise customers will maintain existing access, individual and free users must transition to the new platform before Gemini CLI stops serving requests on June 18, 2026.

Google is expanding its smart home ecosystem by launching a full-stack Gemini AI offering that integrates advanced camera intelligence, natural language queries, and daily activity summaries. This initiative provides service providers and hardware manufacturers with turnkey reference designs and APIs to build proactive, branded services without extensive research and development. Ultimately, the program aims to move beyond basic device control toward an AI-native home that can understand context and care for users' needs in real time.

Google has announced the launch of version 0.1.0 of the Agent Development Kit (ADK) for Kotlin, alongside a specialized ADK library for Android. This open-source framework simplifies the creation of AI agents by managing complex orchestration, session sharing, and error handling across cloud and edge environments. The release supports hybrid orchestration, enabling developers to build multi-agent systems where a cloud-based model can seamlessly offload specific tasks to local, on-device models like Gemini Nano to enhance user privacy.

We are excited to bring Express checkout with Google Pay for Android native apps enabling developers...

Google Pay is evolving for "agentic commerce" by introducing the Universal Commerce Protocol and a new MCP server that allows AI agents to manage integrations and analyze trends. New Android updates introduce dynamic callbacks for seamless express checkouts and extend payment support into social media apps via WebViews. Additionally, the platform is launching cross-device biometric authentication and new transaction signals to help merchants reduce friction and optimize processing costs.

Google has announced the new Google Pay & Wallet Developer MCP server, an open-standard tool designed to securely connect AI development assistants and IDEs with real-time API and account context. The server allows developers to remain within their development environment to search official documentation, validate Wallet pass definitions, check integration status, and manage merchant accounts. Ultimately, this integration aims to reduce friction and accelerate development workflows by minimizing context switching and providing up-to-date, grounded AI support.

The Google Tunix Hackathon on Kaggle challenged developers to transform small, non-reasoning base models into general reasoning engines using Kaggle TPUs and a limited compute budget. The winning teams achieved this by implementing multi-stage post-training pipelines that combined Supervised Fine-Tuning (SFT) with advanced alignment techniques like GRPO and SimPO. Ultimately, the competition democratized AI development by proving that highly capable, structured reasoning models can be successfully trained by the community using accessible, open-source resources.

Google DeepMind’s Gemma 4 12B model brings agentic, multimodal AI capabilities to everyday laptops with 16GB of RAM, enabling local data processing and visual insight generation. Users can leverage this model on macOS through the Google AI Edge Gallery for dynamic Python code execution and visualization, as well as via Google AI Edge Eloquent for completely offline voice dictation and text editing. Additionally, developer workflows are enhanced by the LiteRT-LM CLI's new serve command, which creates an industry-compatible local endpoint to power fully-local AI tools and agents.

The newly released Gemma 4 12B is a dense, multimodal model designed for high-performance local AI execution on consumer devices. By introducing a novel, encoder-free architecture, it bypasses traditional visual and audio encoders to feed multimodal data directly into the LLM backbone.

Google has announced the Google Colab Command-Line Interface (CLI), a new tool that allows developers and AI agents to connect local terminals to remote Colab runtimes for frictionless execution. The lightweight CLI enables users to easily request high-powered GPUs, run local Python scripts remotely, and seamlessly retrieve artifact logs or models like fine-tuned Gemma 3 adapters. By integrating directly into standard terminal environments, the tool is highly programmable and ready to be used by AI agents such as Antigravity or Claude Code to manage complex machine learning pipelines.

DiffusionGemma is an experimental text-generation model built on the Gemma 4 architecture that uses diffusion-based parallel generation instead of token-by-token autoregression, enabling much faster inference, bidirectional context awareness, and real-time self-correction while remaining deployable on consumer GPUs. Its architecture generates and refines 256-token blocks in parallel through iterative denoising, allowing it to handle complex constraint-based tasks such as Sudoku more effectively than traditional language models and demonstrating strong gains from fine-tuning. The model integrates with vLLM and other popular inference frameworks, giving developers access to a new non-autoregressive approach that combines high performance, efficient long-context scaling, and straightforward customization and deployment.

Google is enhancing Sign in with Google by introducing new OIDC standard claims—specifically auth_time and amr (Authentication Methods Reference) to provide developers with deeper session metadata. These updates allow verified apps to verify the "freshness" of a user's login and the specific authentication methods used (such as MFA or hardware keys), enabling more dynamic, risk-based access controls. By leveraging these federated identity signals, platforms can better prevent account takeover and fraud while implementing granular security policies like step-up authentication for sensitive actions.

Google has officially launched the TPU Developer Hub, a centralized educational resource designed to help model builders and developers maximize the performance of Google Cloud TPUs. The hub offers code-first resources, open-source recipes, and deep-dive documentation covering hardware architecture, software optimization, debugging, parallelism, and networking. These materials are tailored for both human developers and AI-assisted tools to streamline everything from large-scale training to low-latency inference workloads.

An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...

This post introduces three architectural patterns designed to integrate Model Context Protocol (MCP) Apps and Agent-to-User Interface (A2UI) to solve the tradeoff between highly custom iframe environments and native, declarative rendering. By combining these approaches, developers can serve native-feeling UIs directly over MCP servers, embed complex and stateful iframe apps securely inside declarative views, or inject generative UI components into legacy systems. Ultimately, these hybrid frameworks empower engineering teams to deliver secure, performant, and brand-consistent agentic user experiences tailored to their specific project constraints.

The Google Tensor ML SDK is graduating to its Beta phase, allowing developers to build and deploy high-performance machine learning models directly onto the TPU of Google Pixel 10 devices. By integrating with LiteRT, Google's edge deployment framework, the SDK provides a unified workflow for developers to convert, compile, and run PyTorch or TFLite models with robust fallback options. Additionally, a new model garden offers over 100 classic and generative AI models, including Gemma 3, enabling low-latency, private features like speech recognition, computer vision, and text generation.

The Google AI Edge Gallery app has expanded its on-device AI capabilities by introducing experimental support for the open-source Model Context Protocol (MCP) on Android, allowing Gemma 4 to coordinate complex tasks across external data sources like Google Workspace and Google Maps. To enable more proactive and persistent user interactions, the update adds a "Schedule Notification" skill for automating routines and a persistent chat history feature that restores long session contexts nearly instantly. Driven by an open-source toolkit, the platform encourages community developers to build and share custom utility-focused workflows, prompt configurations, and tool integrations via its GitHub repository.

Google AI Edge’s LiteRT-LM provides a production-proven, highly optimized infrastructure for running Gemma 4 across cross-platform mobile and edge environments. It actively unlocks the model's native multimodal and agentic features on-device by utilizing memory-efficient dynamic loading, Multi-Token Prediction for up to a 2.2x speedup, and advanced orchestration tools like Thinking Mode and Constrained Decoding. Furthermore, the engine is rapidly expanding its integration surfaces beyond Android, introducing new native Swift APIs for Apple ecosystems and WebGPU-accelerated JavaScript APIs for high-performance, serverless browser inference.

Google announced the transition from assistive AI to independent agents, highlighting the launch of the Gemini 3.5 series and major updates to its Antigravity agent-first development platform. For mobile developers, the post introduces new Android CLI tools, the Android Bench evaluation leaderboard, and an automated Migration agent designed to rapidly convert various frameworks into native Kotlin code. Web development is also being transformed through Chrome DevTools for agents, the HTML-in-Canvas API, and the proposal of WebMCP, an open web standard that enables browser-based AI agents to execute complex tasks.

The Google Cloud and NVIDIA developer community is celebrating its first anniversary with 100,000 members and a renewed focus on providing builders with advanced AI infrastructure and resources. To accelerate development, the community offers curated learning pathways for mastering LLM optimization, GPU-accelerated data analytics, and monthly expert-led webinars. Moving into its second year, the initiative will expand to include hands-on labs, engineering events, and specialized content focused on the growth of agentic AI.

Google is unifying its AI terminal tools by transitioning the community-focused Gemini CLI into Antigravity CLI, a new agent-first platform built for complex, multi-agent workflows. This new Go-based tool offers faster execution, asynchronous processing, and a unified architecture that syncs with the Antigravity 2.0 desktop application. While enterprise customers will maintain existing access, individual and free users must transition to the new platform before Gemini CLI stops serving requests on June 18, 2026.

Google is expanding its smart home ecosystem by launching a full-stack Gemini AI offering that integrates advanced camera intelligence, natural language queries, and daily activity summaries. This initiative provides service providers and hardware manufacturers with turnkey reference designs and APIs to build proactive, branded services without extensive research and development. Ultimately, the program aims to move beyond basic device control toward an AI-native home that can understand context and care for users' needs in real time.

Google has announced the launch of version 0.1.0 of the Agent Development Kit (ADK) for Kotlin, alongside a specialized ADK library for Android. This open-source framework simplifies the creation of AI agents by managing complex orchestration, session sharing, and error handling across cloud and edge environments. The release supports hybrid orchestration, enabling developers to build multi-agent systems where a cloud-based model can seamlessly offload specific tasks to local, on-device models like Gemini Nano to enhance user privacy.

We are excited to bring Express checkout with Google Pay for Android native apps enabling developers...

Google Pay is evolving for "agentic commerce" by introducing the Universal Commerce Protocol and a new MCP server that allows AI agents to manage integrations and analyze trends. New Android updates introduce dynamic callbacks for seamless express checkouts and extend payment support into social media apps via WebViews. Additionally, the platform is launching cross-device biometric authentication and new transaction signals to help merchants reduce friction and optimize processing costs.

Google has announced the new Google Pay & Wallet Developer MCP server, an open-standard tool designed to securely connect AI development assistants and IDEs with real-time API and account context. The server allows developers to remain within their development environment to search official documentation, validate Wallet pass definitions, check integration status, and manage merchant accounts. Ultimately, this integration aims to reduce friction and accelerate development workflows by minimizing context switching and providing up-to-date, grounded AI support.

The Google Tunix Hackathon on Kaggle challenged developers to transform small, non-reasoning base models into general reasoning engines using Kaggle TPUs and a limited compute budget. The winning teams achieved this by implementing multi-stage post-training pipelines that combined Supervised Fine-Tuning (SFT) with advanced alignment techniques like GRPO and SimPO. Ultimately, the competition democratized AI development by proving that highly capable, structured reasoning models can be successfully trained by the community using accessible, open-source resources.

Google DeepMind’s Gemma 4 12B model brings agentic, multimodal AI capabilities to everyday laptops with 16GB of RAM, enabling local data processing and visual insight generation. Users can leverage this model on macOS through the Google AI Edge Gallery for dynamic Python code execution and visualization, as well as via Google AI Edge Eloquent for completely offline voice dictation and text editing. Additionally, developer workflows are enhanced by the LiteRT-LM CLI's new serve command, which creates an industry-compatible local endpoint to power fully-local AI tools and agents.

The newly released Gemma 4 12B is a dense, multimodal model designed for high-performance local AI execution on consumer devices. By introducing a novel, encoder-free architecture, it bypasses traditional visual and audio encoders to feed multimodal data directly into the LLM backbone.

Google has announced the Google Colab Command-Line Interface (CLI), a new tool that allows developers and AI agents to connect local terminals to remote Colab runtimes for frictionless execution. The lightweight CLI enables users to easily request high-powered GPUs, run local Python scripts remotely, and seamlessly retrieve artifact logs or models like fine-tuned Gemma 3 adapters. By integrating directly into standard terminal environments, the tool is highly programmable and ready to be used by AI agents such as Antigravity or Claude Code to manage complex machine learning pipelines.

DiffusionGemma is an experimental text-generation model built on the Gemma 4 architecture that uses diffusion-based parallel generation instead of token-by-token autoregression, enabling much faster inference, bidirectional context awareness, and real-time self-correction while remaining deployable on consumer GPUs. Its architecture generates and refines 256-token blocks in parallel through iterative denoising, allowing it to handle complex constraint-based tasks such as Sudoku more effectively than traditional language models and demonstrating strong gains from fine-tuning. The model integrates with vLLM and other popular inference frameworks, giving developers access to a new non-autoregressive approach that combines high performance, efficient long-context scaling, and straightforward customization and deployment.

Google is enhancing Sign in with Google by introducing new OIDC standard claims—specifically auth_time and amr (Authentication Methods Reference) to provide developers with deeper session metadata. These updates allow verified apps to verify the "freshness" of a user's login and the specific authentication methods used (such as MFA or hardware keys), enabling more dynamic, risk-based access controls. By leveraging these federated identity signals, platforms can better prevent account takeover and fraud while implementing granular security policies like step-up authentication for sensitive actions.

Google has officially launched the TPU Developer Hub, a centralized educational resource designed to help model builders and developers maximize the performance of Google Cloud TPUs. The hub offers code-first resources, open-source recipes, and deep-dive documentation covering hardware architecture, software optimization, debugging, parallelism, and networking. These materials are tailored for both human developers and AI-assisted tools to streamline everything from large-scale training to low-latency inference workloads.

An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...

This post introduces three architectural patterns designed to integrate Model Context Protocol (MCP) Apps and Agent-to-User Interface (A2UI) to solve the tradeoff between highly custom iframe environments and native, declarative rendering. By combining these approaches, developers can serve native-feeling UIs directly over MCP servers, embed complex and stateful iframe apps securely inside declarative views, or inject generative UI components into legacy systems. Ultimately, these hybrid frameworks empower engineering teams to deliver secure, performant, and brand-consistent agentic user experiences tailored to their specific project constraints.