---
title: "Google"
description: "Global technology leader developing innovative software, cloud services, and AI solutions across mobile, web, and enterprise platforms"
---

# Google
> Global technology leader developing innovative software, cloud services, and AI solutions across mobile, web, and enterprise platforms

## Articles

### [Building scalable AI agents with modular prompt transpilation](https://yomu.fyi/post/building-scalable-ai-agents-with-modular-prompt-transpilation.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Simerus Mahesh
- Published: Jul 16, 2026
- Type: Problem & solution

Monolithic system prompts become difficult to reason about, test, and change as teams add safety policies, domain rules, formatting requirements, and escalation behaviors. The proposed approach treats prompts as software artifacts: modular skill files are composed with includes, variables, and macros, then transpiled into deterministic artifacts. Build-time validation checks missing imports, undefined variables, and circular dependencies, while dependency graphs and CI/CD golden-file comparisons catch drift before deployment. At runtime, progressive disclosure keeps a stable control plane loaded while agents retrieve task-specific skills, reducing context exhaustion and noise. Agents can propose new skill modules or import changes through pull requests, but human review and evaluation remain part of the merge process.


### [Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search](https://yomu.fyi/post/expanding-choice-in-gemini-enterprise-agent-platform-introducing-groun.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Guangsha Shi
- Published: Jul 16, 2026
- Type: Announcement

Gemini Enterprise Agent Platform now natively integrates Parallel Web Systems as a web-grounding provider, expanding developers’ choice of sources for real-time, verifiable information. Grounding with Parallel Web Search connects Gemini models to Parallel’s structured, LLM-optimized search results, combines the provider’s web index with Gemini’s prompt decomposition and synthesis, and adds precise citation annotations. The capability is available through the Gemini API and Agent Studio, and can be subscribed to through Google Cloud Marketplace with usage charged on an existing Google Cloud invoice; zero data retention is available for sensitive workloads. Developers can make programmatic calls at scale, extract and cache web data, post-process results with other LLMs, and build catalog enrichment, autonomous-agent, and multi-agent workflows. Setup requires subscribing, accepting the terms, reviewing pricing, and selecting Parallel Web Search under Agent Studio’s partner-grounding settings.


### [Systems Engineering Playbook: Optimizing Qwen 3.5-397B MoE on Ironwood (TPU7x)](https://yomu.fyi/post/systems-engineering-playbook-optimizing-qwen-3-5-397b-moe-on-ironwood.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Google for Developers
- Published: Jul 14, 2026
- Type: Benchmark

Serving Qwen3.5-397B, a 397-billion-parameter sparse Mixture-of-Experts model, on Ironwood (TPU v7x) requires fitting roughly 400 GB of weights into HBM while sustaining utilization across contrasting workloads. The report applies a modular, model-agnostic playbook built from reusable JAX/Pallas kernels and hardware-aware cost models, focusing new work on Gated DeltaNet linear attention and Attention Data Parallelism while integrating changes into vLLM and SGLang. The architecture activates exactly 17 billion parameters per token and combines 15 repeating blocks of three GDN layers and one GQA layer. Benchmarks use a single host with four Ironwood chips, eight logical devices, 8K/1K prefill-heavy and 1K/8K decode-heavy workloads, and concurrency tiers from 64 to 512. Between April and June 2026, stated optimizations improved inference performance by approximately 4.7x for 512-concurrency prefill-heavy workloads and 3.1x for decode-heavy workloads.


### [LiteRT.js, Google's high performance Web AI Inference](https://yomu.fyi/post/litert-js-google-s-high-performance-web-ai-inference.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Ping Yu, Marko Ristić, Matthew Soulanille, Chintan Parikh
- Published: Jul 9, 2026
- Type: Announcement

Google announces LiteRT.js, a JavaScript binding of LiteRT that runs .tflite AI and ML models directly in web browsers. It targets local inference for JavaScript and TypeScript applications, with privacy benefits, zero server costs, and low latency, while providing smoother deployment for existing .tflite models. Through WebAssembly, it exposes LiteRT’s optimized runtime across XNNPACK CPU, ML Drift with WebGPU, and experimental WebNN for NPUs; LiteRT Torch supports one-step PyTorch conversion, and AI Edge Quantizer enables layer-specific quantization. The initial release includes the @litertjs/core npm package, demos, and examples covering YOLO26, depth estimation, and Real-ESRGAN upscaling. In controlled benchmarks on a 2024 Apple MacBook Pro with M4, it outperformed other web runtimes by up to 3x, while WebGPU or WebNN delivered 5–60x speedups over CPU execution; results may vary with hardware, thermal conditions, and browser drivers.


### [Bridging the Domain Gap: AI Race Coach built with Antigravity and Gemini](https://yomu.fyi/post/bridging-the-domain-gap-ai-race-coach-built-with-antigravity-and-gemin.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: David Mclaughlin, Ajeet Mirwani
- Published: Jul 8, 2026
- Type: Explainer

At Sonoma Raceway, Google Developer Experts built an AI-powered race coach to turn vehicle telemetry into split-second driving advice, extending an earlier pilot that handled basic telemetry. The system uses Antigravity for stateful orchestration and ingestion, Python and Jetpack Compose for mobile processing and display, Agent Development Kit for agent coordination, and hybrid Gemma 4 edge reasoning plus Gemini API cloud analysis. A custom USB interface connected a Pixel 10 directly to the vehicle telemetry network, providing a 10 Hz stream from hundreds of sensors, while an activated on-device TPU reached 40 tokens per second for local coaching alerts delivered through text-to-speech. The test identified a throttle application zone in Turn 2 associated with a 0.1-second gain, and the initiative is scheduled to continue at Interlagos, Brazil.


### [We terminated a TPU mid-training and it recovered in seconds:  Introduction to elastic training with MaxText](https://yomu.fyi/post/we-terminated-a-tpu-mid-training-and-it-recovered-in-seconds-introduct.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Luke Baumann, Abhinav Singh, Ivan Nardini
- Published: Jul 6, 2026
- Type: Tutorial

Distributed training on multiple TPU machines normally fails as a whole when one worker disappears: collective communication times out, processes exit, and the job restarts from a checkpoint. The article demonstrates elastic training with JAX, MaxText, PathwayS, Orbax, and Cloud TPUs on GKE, using a single CPU-side controller that remains alive while TPU workers execute compiled programs. A Pathways elastic\_retry wrapper catches the failure, cleans up partial state, restores the last viable checkpoint, and re-enters the training function without relaunching the workload; the demo returned to training in under two minutes, with no JobSet restarts, after losing 88 steps. It also contrasts pause-and-resume with replica resize, explains direct checkpoint persistence through ENABLE\_PATHWAYS\_PERSISTENCE=1, and describes planned snapshot-based recovery from host memory.


### [Build agentic full-stack apps with Genkit](https://yomu.fyi/post/build-agentic-full-stack-apps-with-genkit.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Chris Gill
- Published: Jul 1, 2026
- Type: Announcement

Genkit Agents is an open-source, full-stack foundation for conversational AI and agentic applications, supporting TypeScript, Go, Dart, and Python. The Agents API packages message history, tool loops, streaming, persistence, and frontend communication behind a shared interface: agents can run in process or behind HTTP, and clients use chat() through remoteAgent(). Developers can choose client-managed state or session-store-backed snapshots, resume by session ID, branch from snapshot ID, stream text, custom state, and artifacts, and pause tools for human approval. The framework also supports detached long-running work, specialist delegation, persistence choices including in-memory, file, Firestore, and custom stores, plus an Agent Runner in the Developer UI. The Agents API is in preview for TypeScript and Go, with breaking changes possible in minor releases.


### [ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available](https://yomu.fyi/post/ml-development-in-vs-code-with-google-cloud-power-workbench-extension.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Andrii Lobanov, Alex Kallaur, Diego Granados
- Published: Jul 1, 2026
- Type: Announcement

Google Cloud has launched the Google Cloud Workbench Notebooks extension for VS Code, allowing developers to use managed cloud notebook environments from a local IDE. Workbench, described as a managed Jupyter platform optimized for data science, can provide access to Google Cloud's AI-optimized infrastructure and high-performance compute when local machines need more capacity. The workflow connects through VS Code's Extensions view, the Jupyter extension, and a notebook's Select Kernel menu, where users choose Google Cloud, Workbench, authenticate, select a project, and choose an active instance. The extension runs notebooks on Workbench instances, preserves local settings and extensions, is fully open-sourced, and is available through the VS Code Marketplace, with GitHub contributions invited.


### [Why we built ADK 2.0](https://yomu.fyi/post/why-we-built-adk-2-0.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Swapnil Agarwal, Alan Blount, Frank Guan
- Published: Jul 1, 2026
- Type: Explainer

ADK 2.0 addresses the structural challenge of moving AI agents from prototypes into production, where infinite loops, hallucinated bypasses, and failures without clean exceptions make model-led orchestration unreliable. Because language models are slow, costly, and variable for routing, scheduling, and error handling, the release separates execution routing from language processing and reserves LLMs for ambiguous or cognitive steps. Building on ADK v1’s support for Python, Java, Go, TypeScript, and Kotlin, it adds a structured workflow runtime and task-collaboration model, with Workflows available in Python since March and newly launched for Go. Deterministic graphs combine tool calls, Human-in-the-Loop steps, and specialized agents with explicit branching and error handling. Dynamic workflows extend this model with native Python and asyncio control flows for retries, branching, and reusable sub-workflows.


### [Build reliable multi-agent applications with ADK Go 2.0. Discover our new graph-based workflow engine, built-in human-in-the-loop, and dynamic orchestration](https://yomu.fyi/post/build-reliable-multi-agent-applications-with-adk-go-2-0-discover-our-n.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Toni Klopfenstein, Sampath Kumar Maddula
- Published: Jun 30, 2026
- Type: Announcement

ADK for Go 2.0 introduces a graph-based workflow engine for composing multi-agent applications whose execution can classify, branch, fan out, gather results, pause for human input, retry, and loop. Graphs consist of typed nodes and routed edges, with constructors for functions, emitting functions, agents, tools, joins, parallel workers, state-bound nodes, dynamic orchestration, and nested workflows; a scheduler handles concurrency, persistence, backpressure, cancellation, and resumption. Its built-in human-in-the-loop primitive validates responses, supports handoff or re-entry, and can resume durably after process restarts, while agent modes and a unified node runtime align single agents with full graphs. The release is highly additive for ADK 1.0 users, though context and event APIs require mechanical migrations, and it provides workflow examples for basic, routing, dynamic, HITL, and complex graphs.


### [Driving the Agent Quality Flywheel from Your Coding Agent](https://yomu.fyi/post/driving-the-agent-quality-flywheel-from-your-coding-agent.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Dima Melnyk, Jason Dai
- Published: Jun 30, 2026
- Type: Tutorial

The quality-flywheel skill addresses the gap between prompt tweaks that improve a few examples and evidence that an agent improved without regressions. A coding agent orchestrates dataset preparation, inference, trace grading, failure analysis, and optimization, using Google's adaptive AutoRaters or custom metrics while keeping the optimizer and evaluator independent. In a travel-concierge example, the revision\_honored rubric found that 21% of cases ignored mid-conversation trip changes, exceeding the stated action threshold. A separate software-bug-assistant cycle raised tool-disclosure responses from 0% to 96% across 15 cases, while production traces and online monitors extend the same loop beyond synthetic scenarios. Human approval remains required, and adaptive scores are treated as directional signals rather than absolute grades.


### [Measuring What Matters with Jules](https://yomu.fyi/post/measuring-what-matters-with-jules.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Nghi Bui, Georgios Evangelopoulos, Zack Elliott
- Published: Jun 22, 2026
- Type: Benchmark

Proactive coding agents need evaluation beyond task completion because existing public benchmarks such as SWE-Bench do not measure goal-oriented insight policies: deciding what matters, supporting evidence, and whether to interrupt or stay silent. To create preliminary ground truth, researchers clustered 705 bugs and 1,178 CLs from internal Google codebases using temporal proximity and semantic similarity, reverted each codebase to its pre-fix state, and gave the agent up to three exploration rounds. An LLM scored predicted insights from 1 to 5 against clustered bug targets, while success used average top score and Hit@K. With one exploration round, the agent averaged 4.5 out of 5; increasing the budget from two to three rounds raised Hit@5 from 33% to 57%, although the authors describe the findings as preliminary and are expanding the evaluation to public GitHub data and richer context streams.


### [Build Cross-Language Multi-Agent Team with Google’s Agent Development Kit and A2A](https://yomu.fyi/post/build-cross-language-multi-agent-team-with-google-s-agent-development.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Shubham Saboo, Eric Dong
- Published: Jun 22, 2026
- Type: Tutorial

The post presents a contract-compliance pipeline in which a Python agent uses Gemini to extract contract terms and a Go service applies deterministic corporate-policy checks. It connects the services with the Agent2Agent (A2A) protocol and uses Google's Agent Development Kit (ADK), including RemoteA2aAgent, to expose the remote validator as a local sub-agent in a SequentialAgent. A2A supplies discovery through Agent Cards at /.well-known/agent.json, JSON-RPC 2.0 messaging through message/send, typed Message Parts, and task states such as submitted, working, completed, and failed. Pipeline state is shared through ADK's ToolContext.state, with failures transitioning to MANUAL\_REVIEW for human legal review when the Go service is unavailable. The accompanying open-source application demonstrates live handoffs, policy verdicts, and simulated network faults, while the conclusion emphasizes language independence, decoupling, resilience, and deterministic auditing.


### [How A2A is Building a World of Collaborative Agents](https://yomu.fyi/post/how-a2a-is-building-a-world-of-collaborative-agents.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Alan Blount, Frank Guan, Nick Losier
- Published: Jun 18, 2026
- Type: Announcement

A2A, launched one year before this June 18, 2026 update, is presented as a common language for conversational, dynamic agents that need to collaborate and hand off tasks securely rather than behave like stateless tools. The protocol uses black-box handoffs to keep sensitive data and proprietary processes encapsulated, prevent context pollution, support clarifying exchanges, and distribute specialized workloads across teams or services. Foldrun demonstrates this model by exposing protein-structure prediction as a standalone agentic interface that manages long-running tasks, adjusts parameters based on prediction confidence, and selects among AlphaFold 2, OpenFold 3, and Boltz-2 depending on the molecule. In an A2A-compliant environment, users can register Foldrun and delegate work in natural language without custom glue code, while a primary agent handles other research tasks. The post also cites applications in commerce, streaming, IT operations, and regulated telecom, while Python and Go SDKs are 1.0 GA.


### [Announcing the Agentic Resource Discovery specification](https://yomu.fyi/post/announcing-the-agentic-resource-discovery-specification.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Junjie Bu, Srinivas Krishnan
- Published: Jun 17, 2026
- Type: Announcement

Agentic Resource Discovery (ARD) is an open specification for publishing, discovering, and verifying AI capabilities across the web, addressing the lack of a standard that works across organizations and ecosystems. It uses organization-hosted catalogs, including an ai-catalog.json file at a well-known domain path, plus federated registries that crawl, index, and return capabilities with trust metadata. Clients can query a registry with plain-language intent or fetch a known catalog directly, verify the publisher’s cryptographic identity, then connect at runtime through the capability’s native protocol or API. Google Cloud is bringing ARD to Gemini Enterprise Agent Platform through Agent Registry, which supports hosted discovery and governance features including namespaced URNs, egress policies, specification pinning, and Agent Identity trust-manifest verification. The specification is available under Apache 2.0, with native Agent Platform support planned in the coming months.


### [A2UI + MCP Apps: Combining the best of declarative and custom agentic UIs](https://yomu.fyi/post/a2ui-mcp-apps-combining-the-best-of-declarative-and-custom-agentic-uis.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Google A2UI Team, Ido Salomon, Liad Yosef
- Published: Jun 17, 2026
- Type: Explainer

Agentic UIs must balance deep customization with seamless integration: MCP Apps provide standard-web flexibility in an iframe, while A2UI renders declarative JSON through the host application's native components. The post presents three architectural patterns for combining them, including A2UI over MCP, where MCP servers return application/a2ui+json resources for native rendering and can separate static MCP Resources from dynamic Tool calls. This arrangement avoids iframe-related visual fragmentation, keeps backend tool access separate from frontend rendering, and supports rendering across React, Flutter, or Angular through a predefined component catalog. The broader combination also reserves MCP Apps for highly tailored experiences and describes A2UI in MCP Apps, where an embedded app can process A2UI responses and route user actions through an AppBridge; the conclusion recommends choosing among patterns according to project constraints.


### [Unlocking the Power of the TPU Stack: Introducing our new Developer Hub](https://yomu.fyi/post/unlocking-the-power-of-the-tpu-stack-introducing-our-new-developer-hub.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Keelin McDonell
- Published: Jun 16, 2026
- Type: Announcement

Google Cloud has launched the TPU Developer Hub, an educational resource for model builders, optimizers, and developers working with TPU infrastructure and its supporting software stack. It organizes guidance across the developer lifecycle, covering pre-training, post-training, and inference, with material on hardware architecture, infrastructure consumption modes, and software capabilities including specialized compiler technology and XLA. Resources also address PyTorch migration and deployment, XProf-based tracing and observability, multi-chip parallelism, Pallas kernels, KV cache offloading, networking, and security for distributed workloads. The hub combines interactive Colabs, open-source code recipes, and deep-dive documentation, with content intended for both manual browsing and AI-assisted development. Its stated purpose is to help users select appropriate TPU infrastructure, diagnose bottlenecks, optimize model performance, and move from concepts toward production through practical, regularly updated guidance.


### [Enhance Security and Trust: New Session Metadata in Sign in with Google](https://yomu.fyi/post/enhance-security-and-trust-new-session-metadata-in-sign-in-with-google.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Sergei Akulich, Brian Daugherty
- Published: Jun 16, 2026
- Type: Announcement

Sign in with Google now provides verified applications with two OpenID Connect session metadata claims, auth\_time and amr, in ID tokens. auth\_time records the last successful Google authentication time, distinguishing session freshness from token issuance, while amr is a JSON array identifying authentication methods such as pwd, mfa, hwk, swk, tel, and sms. The claims are available across Android, iOS, and web client and server applications, and can support audit logging, step-up authentication based on session age, and authorization rules requiring stronger methods for sensitive operations. Verified applications request them through the standard OIDC claims parameter without significantly changing an existing Sign in with Google authentication flow.


### [DiffusionGemma: The Developer Guide](https://yomu.fyi/post/diffusiongemma-the-developer-guide.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Ian Ballantyne, Omar Sanseviero
- Published: Jun 10, 2026
- Type: Tutorial

DiffusionGemma is an experimental text-generation model and developer guide covering serving, customization, and non-autoregressive generation. Built on the Gemma 4 backbone, its 26B Mixture of Experts architecture activates 3.8B parameters during inference, refines 256-token canvases in parallel, and shifts the bottleneck from memory bandwidth to compute. Block autoregressive diffusion commits denoised 256-token blocks to the KV cache, combining bidirectional denoising with causal prefill for longer sequences; vLLM integration provides an OpenAI-compatible local server. A Sudoku fine-tuning example using Hackable Diffusion reports improvement from roughly 0% base-model success to 80%, while SFT reduces the demonstrated solve from 48 to 12 steps through adaptive stopping. The guide also identifies bidirectional attention and re-noising as mechanisms for global constraint handling and self-correction.


### [Introducing the Google Colab CLI](https://yomu.fyi/post/introducing-the-google-colab-cli.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Spencer Shumway, Tyler Pirtle, Seth Troisi
- Published: Jun 5, 2026
- Type: Announcement

Google announces the Google Colab Command-Line Interface (CLI), a tool that connects a local terminal with remote Colab runtimes for developers and AI agents. It can provision GPUs or TPUs with commands such as colab --gpu A100, execute local Python scripts and ML pipelines remotely with colab exec, recover artifacts and replayable .ipynb logs, and provide interactive access through colab repl or colab console. The source demonstrates an agent-driven QLoRA fine-tuning workflow for google/gemma-3-1b-it on a Text-to-SQL dataset: an Antigravity agent provisions a T4, installs transformers, datasets, peft, trl, bitsandbytes, and accelerate, runs finetune\_run.py, saves a notebook log, downloads model artifacts, and stops the runtime. The resulting safetensors adapter and configuration files can be loaded locally, while the CLI is presented as programmable and usable by terminal-based agents.


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