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Google Cloud has introduced AI-powered Database Operations Agents, featuring an Onboarding Agent that streamlines database setup and an Observability Agent that helps automate troubleshooting, performance optimization, and tuning. Integrated with Gemini Cloud Assist, these agents support multiple database services, including AlloyDB, Bigtable, and Spanner. By Sergio De Simone
The AI that powers Gboard's Rambler is coming to more Google products, including Chrome.
Google Cloud has introduced Gemini Enterprise Agent Platform , a developer platform intended to bring AI agent creation, deployment, runtime operations and governance into one product surface. The April 23, 2026 announcement marks a broader shift in how Google is positioning Gemini for business use: not simply as a model that answers prompts, but as part of a stack for building long-running agents that can carry out defined work across company systems. According to Google Cloud's announcement of Gemini Enterprise Agent Platform , the Agent Platform evolves the services previously associated with Vertex AI into a unified platform. It combines model access, agent development tools, runtime infrastructure and operational controls. Google is also extending the broader Gemini ecosystem through Gemini API previews, Google AI Studio, Antigravity, Android development support, the Gemini app on macOS, Gboard features on Android, and planned Gemini Enterprise for Customer Experience capabilities. The important distinction is that Google is describing a platform for agents that can persist over time, retain relevant context and interact with tools, rather than a collection of isolated chatbot features. For companies exploring automation, that could make it easier to move from one-off AI experiments toward applications designed around repeatable workflows. It does not, however, remove the need to define reliable processes, permissions and human oversight before deploying an agent in a customer or operational workflow. What Gemini Enterprise Agent Platform brings together Google describes the Agent Platform as the runtime and governance layer for production-scale AI agents. It is built around three connected areas: creating agents, running them with context and tools, and observing or controlling their behavior once deployed. Agent Studio provides a low-code interface for building agents. Developers can also use the upgraded Agent Development Kit (ADK) , while the reworked Agent
Google has expanded Gemini in Chrome with a desktop workflow that lets users send a selected part of a web page directly to Gemini. Called Select from screen , the feature is designed for moments when a full page is not the relevant context: a user can draw a box around particular text, an image, or a mixed section of page content and ask Gemini to analyze or act on it in Chrome's side panel. The change makes Gemini more closely embedded in everyday browser work. Rather than manually describing what is on a page or switching between tools, users can identify the exact on-screen material they want Gemini to consider. For teams that regularly research products, review creative assets, compare information, or work from web-based documents, that can make AI assistance more immediate. Its usefulness will still depend on whether Gemini in Chrome is enabled for the user and, for managed environments, how administrators configure access. How Select from screen works Google's official instructions for sharing specific parts of a screen with Gemini in Chrome describe a straightforward process. Users open the Gemini side panel in Chrome, choose Select from screen , then draw around the area they want to share. The chosen content is sent to Gemini as the basis for the next interaction. The important distinction is that the feature is not limited to a single content type. Google says the selected region can contain text and/or images . That gives users a more precise way to supply context from a web page without treating the entire page as the prompt. Workflow element General Gemini interaction in Chrome Select from screen Context provided User supplies a request in the Chrome side panel User selects a defined region of a web page for Gemini Content types Depends on the interaction and context available Selected text, images, or a region containing both Selection method No region-selection step Draw a bounding box around the relevant content Why the workflow matters The value is
This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK . What I Built I built an Autonomous Multi-Agent Handwritten Notes Generator . Students and educators often need clean, visual study guides that resemble real handwritten notes, but manually summarizing technical subjects and formatting them takes hours. This system solves that by combining autonomous web research, structured note extraction, and headless browser rendering. You enter any topic or question, and a coordinated team of AI agents researches the concept, formats it into a notebook layout using Google handwriting fonts ( Caveat ), and captures a high-resolution .png notebook page screenshot. Deployment & Repository Links: GitHub Repository: himanshuyeolecse-jpg / multi-agent-handwritten-notes An autonomous multi-agent system built with LangGraph, Tavily, and Playwright that researches complex topics and renders handwritten-style student study notes into PNG screenshots. multi-agent-handwritten-notes An autonomous multi-agent system built with LangGraph, Tavily, and Playwright that researches complex topics and renders handwritten-style student study notes into PNG screenshots. 🎓 Multi-Agent Handwritten Notes Generator An autonomous multi-agent workflow built using LangGraph , LangChain , Tavily Search , and Playwright . The system researches complex technical concepts and dynamically compiles the findings into styled, handwritten-notebook PNG screenshots. 🏗️ System Architecture [ User Input / Prompt ] │ ▼ [ Researcher Node ] ── (Tavily Web Search & Summarization) │ ▼ [ Note Renderer Node ] ── (HTML/CSS + Google Caveat Font + Playwright Screenshot) │ ▼ [ Critic Node ] ── (Validation Check: Is Output Complete?) │ Approved? ──► No ──► [ Researcher Node ] │ Yes ▼ [ PNG Screenshot Saved ] ⚡ Features Autonomous Research: Uses Tavily API to fetch up-to-date technical context. Dynamic HTML/CSS Rendering: Formats structured summaries into a paper-notebook layout utilizing… View o
Waymo’s Gemini integration in Ojai is being introduced through a limited beta rollout , not as a feature available to every rider. The company’s official updates describe early access for a restricted group of riders, followed by gradual expansion, while Gemini in the Ojai cabin continues to receive interface and product enhancements. Waymo first outlined the rollout in May 2026, saying it would welcome initial riders in select cities, including San Francisco, Phoenix and Los Angeles. Those riders would receive free rides as Waymo collected feedback, with access expected to expand over time. Waymo’s May 2026 Ojai rollout announcement is the primary account of that initial plan. The company’s July 2026 update characterized Gemini in Waymo as a beta feature in the Ojai cabin. It also referred to a redesigned user interface and ongoing enhancements. Taken together, the two announcements establish a phased deployment: early rider feedback first, followed by continued iteration and broader access over time. What Waymo has announced about Gemini in Ojai The official information supports a narrower view of availability than a universal launch. In May, Waymo described a limited group of early riders in three named cities. By July, the company was still describing Gemini in the Ojai cabin as a beta. Rollout stage Waymo’s description What it indicates May 2026 First riders in San Francisco, Phoenix and Los Angeles, with free rides for a limited group while Waymo collected feedback. Initial access was restricted and feedback-led. July 2026 Gemini in Waymo was described as a beta feature in the Ojai cabin, with a redesigned UI and ongoing enhancements. The in-cabin experience remained in active development. Waymo did not provide a universal-access date in the supplied material. Its stated approach is gradual expansion, which means availability should be assessed by the company’s current rider communications rather than by assuming that a beta feature has reached all users. Why
This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK . Finding the perfect, thoughtful gift shouldn't feel like a chore. Whether it's for a birthday, anniversary, or holiday, we all experience gift-buying paralysis: Generic suggestions : "Just buy them a mug or a generic gift card." Budget anxiety : Falling in love with an idea only to find out it costs 3x what you planned to spend. Missing the subtle nuances : Forgetting that someone dislikes clutter, lives in a tiny apartment, or prefers practical experiences over physical objects. To solve this, I built GiftAdvisor . It is an intelligent, consumer-friendly gift recommendation system built with Google Agent Development Kit (ADK) , Gemini ( gemini-3.1-flash-lite ) , and deployed seamlessly to Google Cloud Run . Live Demo & Links Live Cloud Run App : https://gift-advisor-1008832068452.us-central1.run.app GitHub Repository : https://github.com/inusha-thathsara/Multi-Agent-Gift-Idea-Generator-with-Google-ADK What I Built GiftAdvisor transforms unstructured descriptions of a person into tailored, ranked, and strictly budget-compliant gift recommendations. Instead of dumping everything into a single monolithic prompt, GiftAdvisor splits the cognitive load across three specialized AI agents orchestrated via Google ADK: Profile Analyzer Agent : Understands the human behind the prompt (lifestyle, hobbies, aesthetic preferences, and explicit anti-preferences ). Idea Finder Agent : Brainstorms creative, thoughtful candidate gifts across multiple categories with estimated market prices. Budget Filter Agent : Audits estimated prices, filters out anything exceeding the user's hard budget limit, swaps in budget-friendly alternatives, and delivers a ranked curation. Key Highlights & Features Pure Multi-Agent Pipeline : Built using Google ADK's LlmAgent , SequentialAgent , and InMemorySessionService . Zero-Overhead Scale-to-Zero : Deployed to Google Cloud Run with min-instances=0 (scales to zero w
Google has connected Gemini Live with its Deep Research capability, allowing users to begin a multi-step research task by voice, leave it running in the background, and return for a spoken or transcript-based follow-up when the work is complete. The change turns Deep Research from a primarily prompt-led activity into a more conversational mobile workflow, particularly for people who need to capture a research request without staying in the app. The key distinction is not simply voice input. Gemini Live can initiate a research process that continues while a user switches apps or locks their phone. Google describes the resulting experience as a way to talk through research, with a notification when the task has finished and a seamless path back into conversation. The company's Gemini Deep Research overview for Pixel presents the capability as part of a broader effort to make in-depth research more usable on mobile devices. Deep Research itself is designed to do more than provide a single response. Google has documented a workflow in which Gemini develops a research plan, searches across sources, expands its investigation as needed, and produces a structured report with links to sources. Reports can also be exported to Google Docs. Bringing that process into Gemini Live changes how a request can begin and how a user can resume it, rather than changing the documented purpose of Deep Research. What changes in the Gemini Live research workflow The update combines conversational initiation with asynchronous execution. A user can explain a complex topic aloud, ask Gemini Live to begin Deep Research, and move on to another task while the system works. When the report is ready, the user can be notified and continue through speech or review the transcript. Workflow element Documented Deep Research experience Gemini Live integration Starting a request A research request can lead to a structured plan. A user can initiate Deep Research by speaking with Gemini Live. Research proce
Google is expanding Gemini into a more structured learning environment with study notebooks , a student-focused workspace for diagnostics, personalized lessons, practice quizzes, flashcards and progress tracking. The rollout turns Gemini from a general-purpose assistant into a tool designed to organize source-based study workflows, beginning with web access worldwide and mobile support planned for later in the summer. In Google's official study notebooks announcement , the company describes a workflow that starts by assessing a learner's baseline knowledge. Gemini can then create smaller lessons tailored to a student's goals and reinforce those lessons with quizzes. The company positions the capability as part of a broader education-focused effort across Gemini and NotebookLM, rather than solely as a standalone product called Student Hub. What Gemini study notebooks add The central change is a dedicated notebook space where students can bring together their course materials and ask Gemini to produce learning activities from them. Google says users can upload sources including notes, PDFs and websites, then generate flashcards and quizzes inside a notebook. Study notebooks can also reference uploaded materials and sources while creating lessons. This structure matters because it moves the interaction beyond one-off prompts. A diagnostic quiz establishes a starting point, personalized bite-sized lessons address a learning goal, and practice quizzes provide a way to revisit material. A dashboard tracks progress within that workflow. Google also points to connections with NotebookLM, including the ability to reference past chats and outputs there. Study notebook element Confirmed role in the workflow Availability described by Google Diagnostic quizzes Establish a learner's baseline knowledge Part of the study-notebook experience Personalized lessons Create bite-sized learning content tailored to goals Part of the study-notebook experience Flashcards and practice quizzes
"We have some really ambitious plans to help you work with AI in Chrome to get things done, and I’ll have more to share soon," Jacob Bank, Relay founder and CEO, said.
This is a submission for Weekend Challenge: Dog Days Edition What I Built PawSafe is an AI-powered web application that helps dog owners answer a simple but important question: "Can my dog eat this?" Users can enter the name of a food, upload a photo, or provide both. PawSafe then analyzes the information using Google's Gemini API and provides a simple safety assessment. The result is categorized into four levels: 🟢 Generally Safe 🟡 Use Caution 🔴 Not Safe ⚪ Unable to Determine Along with the result, PawSafe provides explanations, potential warnings, and safer alternatives when appropriate. My goal was to build something that was useful, simple to understand, and approachable for dog owners rather than making users search through multiple sources every time they encounter an unfamiliar food. Demo Live Demo Code GitHub Repository How I Built It PawSafe is a full-stack application built with: Frontend React Vite Tailwind CSS Lucide React Backend Node.js Express Multer CORS Google Gemini API Deployment Render GitHub The basic flow looks like this: User ↓ Food name / Image / Both ↓ React Frontend ↓ Express API ↓ Google Gemini ↓ Structured Analysis ↓ PawSafe Result Card One of the main technical decisions I made was to keep the Gemini API integration on the backend rather than exposing the API key in the frontend. The frontend sends the user's food information to the Express API. The backend then communicates with Gemini and returns the structured analysis to the frontend. I also wanted the application to support both text and images independently, while still allowing users to provide both when additional context is useful. Prize Categories Best Use of Google AI PawSafe is submitted for the Best Use of Google AI prize category. Google's Gemini API is the core intelligence behind the application. It is used to analyze both text-based and image-based food information and generate a structured safety assessment. The AI response is then presented through PawSafe's interface
This is a submission for Weekend Challenge: Dog Days Edition What I Built WikiPaw is an interactive Wikipedia-hopping game designed around dog breeds! Players are given a target dog breed to reach but start on a Wikipedia page located 2–3 outgoing link hops away from their target. To help navigate the maze of Wiki links, WikiPaw uses Gemini AI as an intelligent guide to evaluate your current page against the target breed and hint at how close you are to reaching your destination Demo Live project coming on Wikipaw wikipaw-demo.mov - Google Drive drive.google.com Code The code is hosted on my github and repo is called wikipaw How I Built It The following points describe how the project works: Core Game Loop: We construct a graph of Wikipedia links starting from a selected dog breed, traversing backwards 2–3 hops to select a fun starting article. Gemini AI Integration: On each page visit, the current Wikipedia article content and target breed details are sent to Gemini AI. The model analyzes semantic similarity, topical relevance, and contextual overlap to calculate a "proximity score" and generate dynamic hints for the player. Frontend/Backend: Built with a clean UI to render stripped Wikipedia content with active internal links while tracking the player's path and hop count. Prize Categories Best Use of Google AI : Leveraged Gemini AI to dynamically calculate semantic proximity between Wikipedia articles and generate context-aware hints for players.
This is a submission for Weekend Challenge: Dog Days Edition What I Built I love dogs. There, I said it. And yet, I am also scared of them. It is a strange combination. I can happily watch dog videos for hours, admire every dog I see on the street, and still instinctively tense up when one suddenly runs towards me. A lot of that comes from a traumatic experience I had with a dog as a child. You can grow up knowing that one experience doesn't define every dog you will ever meet, but sometimes your instincts don't get the memo. While thinking about this challenge, I started wondering if the problem was partly not understanding what I was seeing . If a dog is wagging its tail, what does that actually mean? If it is staring at me, should I move away? If it is barking, is it excited, nervous, protective, or something else? And if a dog is approaching me on a footpath, what should I actually do? That question became Pawsitive . Pawsitive is an interactive learning app for people who feel nervous around dogs. Instead of telling people not to be afraid, it tries to make encounters feel less unpredictable by teaching them how to recognise common body-language signals, understand situations, and make calmer decisions. But then I realised there was another side to the interaction. A dog owner might see their dog happily walking towards someone and think: "Don't worry, he's friendly!" The person approaching might be thinking: "Please don't let that dog come any closer." Both people can be looking at the same dog while experiencing completely different situations. So Pawsitive has two learning paths: people who are nervous around dogs and dog owners . The first helps people understand dogs and build confidence. The second helps owners recognise when someone might be uncomfortable, why giving people space matters, and why "my dog is friendly" doesn't necessarily make an approaching dog less intimidating. That became the idea behind the whole app: Two perspectives. One better inte
Google has launched Gemini 3.7 Flash, providing significant updates for coding, automation, and the development of autonomous agents. The company reduced production pricing to help businesses deploy these tools more affordably. This release comes only three weeks after the previous version, signaling a faster pace for developer-focused updates. Accelerated development cycles and cost reduction strategies The introduction of Gemini 3.7 Flash highlights a shift in how technology providers manage their product lineups. Google is prioritizing rapid iteration for its Flash series, which serves as a high-speed tool for developers. This latest version arrived less than a month after its predecessor, showing the company responds quickly to user feedback. Engineers designed this model to handle software engineering tasks and complex, multi-step workflows with higher precision. Pricing for the new model sits at $0.75 per million input tokens and $3.75 per million output tokens. This represents a reduction of approximately fifty percent compared to the prior version. By lowering the financial barrier, Google aims to make large-scale production deployments more sustainable for businesses. The company describes this version as a reliable workhorse capable of following instructions with greater accuracy than previous iterations. While the Flash series moves quickly, the more advanced Pro models follow a different path. These high-end models, designed for the most difficult reasoning tasks, see less frequent updates. During recent financial discussions, leadership at the company did not provide a specific timeline for the next Pro release. This indicates a growing gap between fast, cost-effective models and the slower development of premium intelligence tiers. Industry trends in model tiering Other companies in the industry are following similar patterns by separating their offerings into distinct categories. For example, some competitors have launched high-end variants alongside
Turning off this setting won't affect invisible benchmarks used to identify an AI generated file.
Gemini 3.6 Flash debuted just 3 weeks ago, but Google says 3.7 has "substantial improvements."
A Gemini workflow for finding and booking GetYourGuide activities has been suggested publicly, including a request for a sunset canyon hike. The specific Gemini and GetYourGuide integration remains unconfirmed. Neither Google nor GetYourGuide had published an accessible first-party announcement, product page, release note, or documentation verifying that the capability is live as of the supplied research date. The more useful question for businesses is not whether a single travel prompt is already available, but what such a connection would represent if it launches. Google has documented Gemini API tooling and partner-integration patterns that let models use external tools for real-time information and actions. GetYourGuide, meanwhile, offers an API for tour content and booking capabilities and has worked on AI-oriented travel experiences. Those foundations make an AI-assisted activity-search and booking workflow technically plausible, even though they do not validate this particular integration. Why a Gemini travel workflow is technically plausible A conversational travel workflow would need more than a model that can generate recommendations. It would need a reliable connection between Gemini, live supplier data, and a transaction system. Gemini's tooling and partner integration materials establish a general model for connecting an AI experience to third-party services, while GetYourGuide's API provides a route to activity inventory and booking functions. In practical terms, a capable workflow could break a request into distinct tasks: Interpret constraints such as destination, date, budget, group size, and accessibility needs. Retrieve current activity information from a travel provider rather than relying on static model knowledge. Present relevant options and ask for missing details before any purchase step. Send a confirmed selection through an authorized booking flow and return the resulting status to the user. That sequence matters because travel inventory i
If you have ever wanted to ship an AI-powered application without managing GPUs, model servers, or scaling infrastructure yourself, this guide is for you. Managed inference simply means letting a cloud provider run the AI model for you: you send a request, the platform handles the compute, and you get a response back. On Google Cloud, the cleanest way to do this today is to pair the Gemini Enterprise Agent Platform (formerly Vertex AI) with Google Cloud Run , dividing responsibilities between the two services. The Agent Platform serves as the orchestration and intelligence engine, while Cloud Run hosts your custom application logic, front-end UIs, or Model Context Protocol (MCP) servers. By the end of this article, you will be able to: Explain the hybrid architecture and why each layer exists Define an AI agent in code using the Agent Development Kit (ADK) Deploy your app layer to Cloud Run with a single command Choose between online and batch inference for your workload Secure and monitor the whole setup in production New to the underlying concept? Start with Google Cloud's primer: What is AI inference? Prerequisites To follow along hands-on, you will need: A Google Cloud project with billing enabled The gcloud CLI installed and authenticated Python 3.10+ and the ADK installed ( pip install google-adk ) You can also read this purely as an architecture walkthrough; every step is explained, not just shown. 1. The Architectural Blueprint This pattern splits your system into independent, auto-scaling tiers: [ Client / Web UI ] ──> [ Cloud Run Service ] (App Logic / Tool Front End) │ ▼ [ Gemini Enterprise Agent Platform — Agent Runtime ] (Orchestration, Intent Analysis, Memory) │ ▼ [ Managed Inference / Model Garden ] (Gemini 3.x Pro / Flash models) Why split it this way? Each tier scales independently and fails independently. Your web front end can handle a traffic spike without touching the model layer, and you can swap models without redeploying your application code
From the Pixel 11 series and a brand new competitor to Apple’s AirTag, here are all the announcements from the Made by Google 2026 event.
Google has placed Gemini 3.5 at the center of a broad product expansion that reaches beyond a single model release. Announced during Google I/O 2026, the update introduces Gemini 3.5 Flash, Gemini Omni, the Spark personal agent, Daily Brief, and a new Neural Expressive interface direction. It also coincides with upgrades to Google's Antigravity agent framework and related developer and enterprise surfaces. The significance is the scope of the rollout. Rather than presenting Gemini 3.5 as an isolated chatbot improvement, Google is extending the family across the Gemini app , multimodal creation, Workspace-connected agent experiences, API and developer tooling. In Google's official Gemini app announcement , the company outlines the new app experiences and their staged availability. Google positions Gemini 3.5 Flash as its flagship model for combining frontier intelligence with fast action. That language is Google's product characterization, but the practical message is clearer: Gemini's next major model family is intended to support responsive interactions across more of Google's products, not just long-form prompts in the consumer app. A Gemini update built around multiple surfaces The Gemini 3.5 rollout combines model, output, agent and interface changes. Each component addresses a different part of how people may use Gemini, from generating multimodal material to receiving proactive assistance and working through connected tools. Gemini component Confirmed role Availability or integration detail Gemini 3.5 Flash Google's flagship model combining frontier intelligence with fast action Part of the Gemini 3.5 rollout Gemini Omni Multimodal video output Beginning rollout to Google AI Plus, Pro and Ultra subscribers Gemini Spark A 24/7 personal agent Integrated with Workspace tools, with a Spark-enabled desktop experience planned for macOS Daily Brief Personalized morning digests Included among the new Gemini app experiences Antigravity enhancements Expanded agent frame