https://developers.googleblog.com/rss/Updates on changes and additions to the Google Developers Blog.en-usWed, 13 May 2026 13:45:43 +0000https://developers.googleblog.com/build-long-running-ai-agents-that-pause-resume-and-never-lose-context-with-adk/How to transition from stateless chatbots to production-grade agents capable of managing long-running enterprise workflows, such as HR onboarding, that span days or weeks. It introduces the Agent Development Kit (ADK) and its architectural shifts, specifically using durable state machines and persistent session storage to ensure an agent never loses context during "idle time" or server restarts. By leveraging event-driven webhooks and multi-agent delegation, the tutorial demonstrates how to build resilient systems that "sleep" during pauses and wake up to resume complex tasks with high reasoning accuracy.https://developers.googleblog.com/build-long-running-ai-agents-that-pause-resume-and-never-lose-context-with-adk/https://developers.googleblog.com/supercharging-llm-inference-on-google-tpus-achieving-3x-speedups-with-diffusion-style-speculative-decoding/Researchers at UCSD have successfully implemented DFlash, a block-diffusion speculative decoding method, on Google TPUs to bypass the sequential bottlenecks of traditional autoregressive drafting. By "painting" entire blocks of candidate tokens in a single forward pass rather than predicting them one-by-one, the system achieved average speedups of 3.13x, with peak performance nearly doubling that of existing methods like EAGLE-3. This open-source integration into the vLLM ecosystem optimizes TPU hardware by leveraging "free" parallel verification and high-quality draft predictions for complex reasoning tasks.https://developers.googleblog.com/supercharging-llm-inference-on-google-tpus-achieving-3x-speedups-with-diffusion-style-speculative-decoding/https://developers.googleblog.com/building-with-gemini-embedding-2/Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for tasks like agentic RAG, visual search, and content moderation. By supporting over 100 languages and offering features like task-specific prefixes and Matryoshka dimensionality reduction, the model provides a highly efficient and accurate foundation for building complex AI agents.https://developers.googleblog.com/building-with-gemini-embedding-2/https://developers.googleblog.com/speeding-up-ai-bringing-google-colossus-to-pytorch-via-gcsfs-and-rapid-bucket/Google Cloud has introduced a high-performance integration that connects Rapid Storage directly to PyTorch via the fsspec interface to eliminate AI training bottlenecks. By utilizing Google’s Colossus architecture and bidirectional gRPC streaming, the solution offers up to 15 TiB/s aggregate throughput and significant reductions in latency. These improvements allow developers to speed up total training time by 23% with zero code changes required beyond updating the storage bucket type.https://developers.googleblog.com/speeding-up-ai-bringing-google-colossus-to-pytorch-via-gcsfs-and-rapid-bucket/https://developers.googleblog.com/building-real-world-on-device-ai-with-litert-and-npu/LiteRT is a production-ready framework designed to help mobile developers unlock the power of Neural Processing Units (NPUs), overcoming the performance and battery limitations of traditional CPU or GPU processing. By providing a unified API that abstracts away hardware complexities, it allows industry leaders like Google Meet and Epic Games to deploy sophisticated AI models for real-time video, animation, and speech recognition with significantly higher efficiency. The platform further supports developers through benchmarking tools and cross-platform compatibility, enabling seamless AI deployment across mobile devices, AI PCs, and industrial IoT hardware.https://developers.googleblog.com/building-real-world-on-device-ai-with-litert-and-npu/https://developers.googleblog.com/agents-cli-in-agent-platform-create-to-production-in-one-cli/Google Cloud has introduced the Agents CLI, a specialized tool designed to bridge the gap between local development and production-grade AI agent deployment. The CLI provides coding assistants with machine-readable access to the full Google Cloud stack, reducing context overload and token waste during the scaffolding process. By streamlining evaluation, infrastructure provisioning, and deployment into a single programmatic backbone, the tool enables developers to move from initial concept to a live service in hours rather than weeks.https://developers.googleblog.com/agents-cli-in-agent-platform-create-to-production-in-one-cli/https://developers.googleblog.com/production-ready-ai-agents-5-lessons-from-refactoring-a-monolith/The blog post outlines the transition of a brittle sales research prototype into a robust production agent using Google’s Agent Development Kit (ADK). By replacing monolithic scripts with orchestrated sub-agents and structured Pydantic outputs, the developers eliminated silent failures and fragile parsing. Additionally, the post highlights the necessity of dynamic RAG pipelines and OpenTelemetry observability to ensure AI agents are scalable, cost-effective, and transparent in real-world applications.https://developers.googleblog.com/production-ready-ai-agents-5-lessons-from-refactoring-a-monolith/https://developers.googleblog.com/a2ui-v0-9-generative-ui/A2UI v0.9 introduces a framework-agnostic standard designed to help AI agents generate real-time, tailored UI widgets using a company’s existing design system. This update simplifies the developer experience with a new Agent SDK for Python, a shared web-core library, and official support for renderers like React, Flutter, and Angular. By decoupling UI intent from specific platforms, the release enables seamless, low-latency streaming of generative interfaces across web and mobile applications. Integrating with broader ecosystems like AG2 and Vercel, A2UI v0.9 aims to move generative UI from experimental demos to production-ready digital products.https://developers.googleblog.com/a2ui-v0-9-generative-ui/https://developers.googleblog.com/maxtext-expands-post-training-capabilities-introducing-sft-and-rl-on-single-host-tpus/MaxText has introduced new support for Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) on single-host TPU configurations, leveraging JAX and the Tunix library for high-performance model refinement. These features enable developers to easily adapt pre-trained models for specialized tasks and complex reasoning using efficient algorithms like GRPO and GSPO. This update streamlines the post-training workflow, offering a scalable path from single-host setups to larger multi-host configurations.https://developers.googleblog.com/maxtext-expands-post-training-capabilities-introducing-sft-and-rl-on-single-host-tpus/https://developers.googleblog.com/subagents-have-arrived-in-gemini-cli/Gemini CLI has introduced subagents, specialized expert agents that handle complex or high-volume tasks in isolated context windows to keep the primary session fast and focused. These agents can be customized via Markdown files, run in parallel to boost productivity, and are easily invoked using the @agent syntax for targeted delegation. This architecture prevents "context rot" by consolidating intricate multi-step executions into concise summaries for the main orchestrator.https://developers.googleblog.com/subagents-have-arrived-in-gemini-cli/https://developers.googleblog.com/new-enhancements-for-merchant-initiated-transactions-with-the-google-pay-api/Google has introduced enhancements to the Google Pay API to provide developers with greater flexibility and control over merchant-initiated transactions (MIT). The update includes new objects within the PaymentDataRequest to specifically handle recurring subscriptions, deferred payments like hotel bookings, and automatic account reloads. By allowing merchants to clearly define future payment terms, these changes improve transparency for users and help reduce transaction declines through better token management. Developers can now implement these features to create more seamless and secure long-term payment experiences.https://developers.googleblog.com/new-enhancements-for-merchant-initiated-transactions-with-the-google-pay-api/https://developers.googleblog.com/get-ready-for-google-io-livestream-schedule-revealed/Google I/O returns May 19–20 to showcase major updates in AI, Android, Chrome, and Cloud, beginning with a keynote on the "agentic era" of development. The event will focus on new tools designed to automate complex workflows and simplify the creation of high-quality, AI-ready applications. Attendees can register to access live sessions, technical demos, and professional development resources both live and on-demand.https://developers.googleblog.com/get-ready-for-google-io-livestream-schedule-revealed/https://developers.googleblog.com/build-better-ai-agents-5-developer-tips-from-the-agent-bake-off/The Google Cloud AI Agent Bake-Off highlights a shift from simple prompt engineering to rigorous agentic engineering, emphasizing that production-ready AI requires a modular, multi-agent architecture. The post outlines five key developer tips, including decomposing complex tasks into specialized sub-agents and using deterministic code for execution to prevent probabilistic errors. Furthermore, it advises developers to prioritize multimodality and open-source protocols like MCP to ensure agents are scalable, integrated, and future-proof against rapidly evolving model capabilities.https://developers.googleblog.com/build-better-ai-agents-5-developer-tips-from-the-agent-bake-off/https://developers.googleblog.com/torchtpu-running-pytorch-natively-on-tpus-at-google-scale/TorchTPU is a new engineering stack designed to provide a native, high-performance experience for running PyTorch workloads on Google’s TPU infrastructure with minimal code changes. It features an "Eager First" approach with multiple execution modes and utilizes the XLA compiler to optimize distributed training across massive clusters. Moving into 2026, the project aims to further reduce compilation overhead and expand support for dynamic shapes and custom kernels to ensure seamless scalability for the next generation of AI.https://developers.googleblog.com/torchtpu-running-pytorch-natively-on-tpus-at-google-scale/https://developers.googleblog.com/supporting-google-account-username-change-in-your-app/Google has updated its account settings to allow U.S. users to change their @gmail.com usernames while keeping all existing account data and inboxes intact. For developers, this means that while old email addresses will remain active as aliases, apps that rely solely on email addresses for identification may face issues with account duplication or lost access. To ensure a seamless user experience, Google recommends migrating to the "subject ID" as the primary user identifier and allowing users to manually update their contact information within app settings.https://developers.googleblog.com/supporting-google-account-username-change-in-your-app/https://developers.googleblog.com/bring-state-of-the-art-agentic-skills-to-the-edge-with-gemma-4/Google DeepMind has launched Gemma 4, a family of state-of-the-art open models designed to enable multi-step planning and autonomous agentic workflows directly on-device. The release includes the Google AI Edge Gallery for experimenting with "Agent Skills" and the LiteRT-LM library, which offers a significant speed boost and structured output for developers. Available under an Apache 2.0 license, Gemma 4 supports over 140 languages and is compatible with a wide range of hardware, including mobile devices, desktops, and IoT platforms like Raspberry Pi.https://developers.googleblog.com/bring-state-of-the-art-agentic-skills-to-the-edge-with-gemma-4/https://developers.googleblog.com/developers-guide-to-building-adk-agents-with-skills/The Agent Development Kit (ADK) SkillToolset introduces a "progressive disclosure" architecture that allows AI agents to load domain expertise on demand, reducing token usage by up to 90% compared to traditional monolithic prompts. Through four distinct patterns—ranging from simple inline checklists to "skill factories" where agents write their own code—the system enables agents to dynamically expand their capabilities at runtime using the universal agentskills.io specification. This modular approach ensures that complex instructions and external resources are only accessed when relevant, creating a scalable and self-extending framework for modern AI development.https://developers.googleblog.com/developers-guide-to-building-adk-agents-with-skills/https://developers.googleblog.com/boost-training-goodput-how-continuous-checkpointing-optimizes-reliability-in-orbax-and-maxtext/The newly introduced continuous checkpointing feature in Orbax and MaxText is designed to optimize the balance between reliability and performance during model training, addressing issues with conventional fixed-frequency checkpointing. Unlike fixed intervals—which can either compromise reliability or bottleneck performance—continuous checkpointing maximizes I/O bandwidth and minimizes failure risk by asynchronously initiating a new save operation only after the previous one successfully completes. Benchmarks demonstrate that this approach significantly reduces checkpoint intervals and results in substantial resource conservation, especially in large-scale training jobs where mean-time-between-failure (MTBF) is short.https://developers.googleblog.com/boost-training-goodput-how-continuous-checkpointing-optimizes-reliability-in-orbax-and-maxtext/https://developers.googleblog.com/adk-go-10-arrives/The launch of Agent Development Kit (ADK) for Go 1.0 marks a significant shift from experimental AI scripts to production-ready services by prioritizing observability, security, and extensibility. Key updates include native OpenTelemetry integration for deep tracing, a new plugin system for self-healing logic, and "Human-in-the-Loop" confirmations to ensure safety during sensitive operations. Additionally, the release introduces YAML-based configurations for rapid iteration and refined Agent2Agent (A2A) protocols to support seamless communication across different programming languages. This framework empowers developers to build complex, reliable multi-agent systems using the high-performance engineering standards of Golang.https://developers.googleblog.com/adk-go-10-arrives/https://developers.googleblog.com/announcing-adk-for-java-100-building-the-future-of-ai-agents-in-java/Google has released version 1.0.0 of the Agent Development Kit (ADK) for Java, introducing powerful new features like Google Maps grounding, built-in URL fetching, and a standardized Agent2Agent protocol for cross-framework collaboration. The update enhances agent control through a new "App" and "Plugin" architecture, which allows for global logging, automated context window management via event compaction, and "Human-in-the-Loop" workflows for action confirmations. Additionally, the release provides robust session and memory services using Google Cloud integrations like Firestore and Vertex AI to manage long-term state and large data artifacts.https://developers.googleblog.com/announcing-adk-for-java-100-building-the-future-of-ai-agents-in-java/