https://developers.googleblog.com/rss/Updates on changes and additions to the Google Developers Blog.en-usWed, 28 Oct 2026 04:51:31 +0000https://developers.googleblog.com/fast-reliable-a2ui/A2UI now supports swappable inference formats, introducing A2UI Express which uses up to 3x fewer tokens and cuts latency by compiling compact Python-style syntax into standard A2UI JSON via Agent SDKs. Developers can also use controlled generation with TypeSafe's Jev system-decision models to render known-shape screens in under 300 ms while smaller models stream text. These updates require no changes to frontend renderers or component catalogs, offering flexible performance levers for generative UI development across Python, TypeScript, and Dart.https://developers.googleblog.com/fast-reliable-a2ui/https://developers.googleblog.com/building-conversational-commerce-with-multi-agent-swarms-on-gcp-a-working-prototype/This document provides a publication template and metadata structure for a technical blog post focused on building Conversational User Interfaces (CUIs) using AI. It outlines key developer focus areas, product stacks, and approval workflows required for publishing technical content through Google Developer Studio. The framework helps engineers organize their guides and solutions to effectively share AI-driven conversational interface designs with the developer community.https://developers.googleblog.com/building-conversational-commerce-with-multi-agent-swarms-on-gcp-a-working-prototype/https://developers.googleblog.com/preview-edit-and-collaborate-on-markdown-md-files-natively-in-google-docs-and-drive/Google Workspace now offers native support for Markdown (.md) files, allowing developers and AI agents to seamlessly collaborate on documentation directly in Google Docs without manual file conversions. This integration preserves native file fidelity for use with Git repositories and developer tools while unlocking standard Docs features like line-level commenting, rendered Drive previews, and tracked version history.https://developers.googleblog.com/preview-edit-and-collaborate-on-markdown-md-files-natively-in-google-docs-and-drive/https://developers.googleblog.com/architecting-governed-self-evolving-ai-agents-on-gemini-agent-platform/Learn how Project Chrysalis combines Google Cloud's Gemini Enterprise Agent Platform, Agent Development Kit (ADK), BigQuery, and a four-gate defense-in-depth security stack (Model Armor, Cloud KMS, Binary Authorization, and OPA) to build governed, self-evolving AI agents that remediate live production outages in 1.8 seconds.https://developers.googleblog.com/architecting-governed-self-evolving-ai-agents-on-gemini-agent-platform/https://developers.googleblog.com/streaming-comes-to-api-gateway-real-time-and-ai-ready-apis-without-changing-your-spec/API Gateway now supports real-time streaming—including Server-Sent Events, WebSockets, gRPC, and chunked transfer encoding—in Public Preview without requiring changes to your existing API specifications. This update allows developers to route long-lived, bi-directional streams and AI workloads directly through the gateway while maintaining consistent authentication, rate limiting, and observability. Additionally, gateways now feature deterministic default hostnames, simplifying environment provisioning and infrastructure-as-code management.https://developers.googleblog.com/streaming-comes-to-api-gateway-real-time-and-ai-ready-apis-without-changing-your-spec/https://developers.googleblog.com/powering-the-agent-development-lifecycle-with-any-framework-from-adk-to-langchain-and-beyond/The Agents CLI Extension System enables developers to use frameworks like LangChain, LangGraph, or custom enterprise stacks while retaining a unified deployment and evaluation workflow. It details a first-party LangChain template that scaffolds, runs, and deploys agents without requiring the Agent Development Kit (ADK). Additionally, it demonstrates how platform teams can author custom extensions to enforce policies such as automated software bill of materials (SBOM) scans during deployment.https://developers.googleblog.com/powering-the-agent-development-lifecycle-with-any-framework-from-adk-to-langchain-and-beyond/https://developers.googleblog.com/announcing-new-vllm-and-sglang-inference-engines-on-tpu/This post announces native PyTorch backends for vLLM and SGLang powered by TorchTPU, enabling developers to run and optimize serving workloads using familiar PyTorch tools. Built in collaboration with open-source project maintainers, these engines include optimized TPU kernel implementations, production-ready features like KV cache management with TPU Sync, and disaggregated serving. Public benchmarks demonstrate state-of-the-art performance and industry-leading cost efficiency on Ironwood TPU across long-context and agentic workloads.https://developers.googleblog.com/announcing-new-vllm-and-sglang-inference-engines-on-tpu/https://developers.googleblog.com/bringing-visual-graph-debugging-to-custom-edge-compilers-with-model-explorer/Google AI Edge Model Explorer now supports custom compiler intermediate representations and open-source adapters like TOSA, VGF, and PTE for on-device machine learning. Developers can leverage hierarchical graph visualization, split-pane views, and per-node trace overlays to catch export regressions and verify hardware delegation. These tools enable engineering teams to transparently debug execution graphs and pinpoint cycle-level latency bottlenecks locally.https://developers.googleblog.com/bringing-visual-graph-debugging-to-custom-edge-compilers-with-model-explorer/https://developers.googleblog.com/integrating-torchtpu-into-torchtitan-and-optimizing-models-for-tpu/TorchTPU integrates a native torch.device("tpu") backend directly into PyTorch, allowing developers to train large-scale models on Google Cloud TPUs without rewriting code in JAX. The TorchTitan TPU fork enhances this with production-grade scaling primitives like SimpleFSDP, Graph Trainer, and custom Pallas kernels that achieve high Model Flops Utilization (MFU) across dense and MoE architectures. Developers can leverage these battle-tested optimization playbooks, recipes, and the Hugging Face modeling backend to efficiently scale their own workloads on TPU infrastructure.https://developers.googleblog.com/integrating-torchtpu-into-torchtitan-and-optimizing-models-for-tpu/https://developers.googleblog.com/unifying-edge-ai-how-coral-npus-open-standard-brings-portable-energy-viable-intelligence-from-model-to-silicon/Google Research has completed the 32-bit open-source Coral NPU RISC-V architecture with the introduction of its Matrix Execution Unit, which delivers up to 1 TOPS while targeting 10 mW average power for edge inference. Alongside this architecture, the release of the v1.0 open-source IREE/MLIR compiler allows developers to take models from JAX, PyTorch, and TensorFlow straight to Coral NPU hardware without writing custom assembly. Developers can now prototype and deploy these multimodal edge applications using new hardware options like the Synaptics Astra SR200 MCU series and globally available Coralboard devkits.12https://developers.googleblog.com/unifying-edge-ai-how-coral-npus-open-standard-brings-portable-energy-viable-intelligence-from-model-to-silicon/https://developers.googleblog.com/step-by-step-guide-build-a-voice-agent-that-never-goes-silent-using-adk/The open-source Agent Development Kit (ADK) and gemini-3.8-live enable developers to build responsive voice agents by implementing non-blocking tools in Python. Developers can use streaming tools to yield progress updates or background execution patterns to keep conversations fluid during long-running backend queries. Additionally, configuring response scheduling behaviors such as WHEN_IDLE, INTERRUPT, or SILENT gives developers precise control over when and how tool results are delivered into the conversation.https://developers.googleblog.com/step-by-step-guide-build-a-voice-agent-that-never-goes-silent-using-adk/https://developers.googleblog.com/native-model-authoring-for-edge-with-litert-tensor-api/Meet the LiteRT Tensor API: giving developers native C++ control over the model graph to unify end-to-end ML computations on heterogeneous edge hardware.https://developers.googleblog.com/native-model-authoring-for-edge-with-litert-tensor-api/https://developers.googleblog.com/three-layers-one-repo-a-tour-of-googleadk-recipes/The google/adk-recipes repository unifies Agent Development Kit (ADK) resources into three structured layers—core/, plugins/, and contrib/—serving as a single source of truth for building AI agents. Developers can leverage reference patterns, install modular vertical skills (such as retail product search and virtual try-on) directly into their coding assistants, and deploy production-ready services across multiple languages. This cohesive structure streamlines the entire workflow from initial scaffolding to full deployment on Google Cloud infrastructure.https://developers.googleblog.com/three-layers-one-repo-a-tour-of-googleadk-recipes/https://developers.googleblog.com/evaluating-ai-agents-moving-past-the-vibe-check/A practical engineering guide and reference architecture for establishing continuous evaluation pipelines for multi-turn AI agents using Gemini Enterprise Agent Platform, Google ADK, and OpenInference standards.https://developers.googleblog.com/evaluating-ai-agents-moving-past-the-vibe-check/https://developers.googleblog.com/announcing-dpop-support-for-google-oauth-securing-refresh-tokens/Google OAuth Platform now supports DPoP (Demonstrating Proof-of-Possession) to sender-constrain refresh tokens, neutralizing Infostealer malware and adversary-in-the-middle attacks. By cryptographically binding long-lived refresh tokens while maintaining short-lived access tokens as standard Bearer tokens, developers achieve robust credential security without incurring high-QPS API performance overhead. Additionally, developers integrating multi-agent or Model Context Protocol (MCP) workloads can use Google Cloud Secure Token Service (STS) to handle secure token delegation across agent chains.https://developers.googleblog.com/announcing-dpop-support-for-google-oauth-securing-refresh-tokens/https://developers.googleblog.com/announcing-mcp-support-in-apigee-hybrid-agentic-tools-that-run-in-your-own-cluster/Apigee hybrid 1.17.0 introduces native Model Context Protocol (MCP) support, enabling the MCP data plane to run directly within your own Kubernetes cluster. This architecture ensures that AI agent request traffic and tool execution remain entirely inside your infrastructure while pulling configuration updates from the control plane. Developers can expose OpenAPI 3.0.x specifications as discoverable MCP tools, leverage existing security policies and API products, and trace end-to-end requests using W3C trace context headers.https://developers.googleblog.com/announcing-mcp-support-in-apigee-hybrid-agentic-tools-that-run-in-your-own-cluster/https://developers.googleblog.com/optimizing-a-200000-line-python-codebase-overnight-with-gemini-4-argon/How our engineering team ran Gemini 4 Argon unattended overnight with .agents/skills and /goal mode to cut in-memory session prep overhead by 40% in long sessions and speed up cold imports by 21% in Google's open-source Python Agent Development Kit (ADK).https://developers.googleblog.com/optimizing-a-200000-line-python-codebase-overnight-with-gemini-4-argon/https://developers.googleblog.com/gemini-4-argon-for-robotics/Gemini 4 Argon is a general-purpose physical reasoning model designed to enhance workflows across the robotics stack with advanced capabilities in physical understanding, code generation, and visual-semantic reasoning. Developers can access and integrate Argon across Google's developer platforms—including Google AI Studio, the Gemini API, and Google Antigravity—to build and test sophisticated robotic applications. By leveraging simulation tools like MuJoCo and code-as-policies, engineers can seamlessly transition from real-world data or sketches to physically verified robot control and self-improvement workflows.https://developers.googleblog.com/gemini-4-argon-for-robotics/https://developers.googleblog.com/ml-drift-next-gen-gpu-aiml-inference-at-the-edge/Google AI Edge has introduced ML Drift, a high-performance, universal GPU compute framework designed to accelerate on-device AI and machine learning inference. By abstracting the complexities of hardware and low-level APIs—including OpenGL ES, OpenCL, Metal, and WebGPU—ML Drift enables developers to build real-time, interactive ML experiences, from sophisticated video effects to generative AI, across mobile and desktop platforms.https://developers.googleblog.com/ml-drift-next-gen-gpu-aiml-inference-at-the-edge/https://developers.googleblog.com/the-outer-loop-insights-first-an-ambient-quality-agent-that-diagnoses-your-production-agent/How do you keep improving an AI agent after the first 80%, once it serves real production traffic and silent quality regressions return HTTP 200 OK? We are open-sourcing AQuA (Ambient Quality Agent), a reference implementation that runs beside your production agent in Google Cloud, sweeps and verifies failure clusters against conversation transcripts, and diagnoses root causes anchored to the exact deployed source snapshot.https://developers.googleblog.com/the-outer-loop-insights-first-an-ambient-quality-agent-that-diagnoses-your-production-agent/