# a2ui Published articles for a2ui. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Android Dev Summit 2026 DevFeed: [Android Dev Summit 2026]() Original publisher: [Read original article]() Author: Jonathon Reid Published: 2026-11-08T14:47:09Z Content type: opinion Language: en Sources: [ProAndroidDev]() Topics: [agentic-coding](), [agentic workflows](), [Android](), [Loop Engineering](), [Android Studio](), [User Interfaces](), [Test-driven development](), [Developer Events]() Tags: [2026](), [a2ui](), [agentic](), [agentic-ai](), [agentic-coding](), [agentic-development](), [agents](), [ai](), [ai-agents](), [android](), [android-app-development](), [android-dev-summit](), [android-studio](), [androiddev](), [automation](), [autonomous](), [autonomous-ai-agents](), [build](), [cli](), [cloud-next](), [code](), [coding](), [dev]() ### AI overview The summit's main engineering theme was support for agentic Android development. The Android CLI is presented as a terminal-based way for agents to edit code, build, test, and verify requirements, with lower execution overhead and context use. The author recommends writing repository agents.md guidance by hand, resolving ambiguities up front with a "grill me" prompt, and having agents demonstrate that requirements are met; verification adds runtime but may reduce rework. Google also announced more than 30 Android Skills for newer features, complex implementations, and security or Play Store requirements. An unnamed emulator prototype is described as faster and lighter than traditional emulators, potentially helping automated development and testing loops. XR workshops highlighted glasses paired with an external computing puck for screen mirroring and multi-window productivity, while adoption remains uncertain. The author interprets Android Studio's smaller role at the event as a possible shift in Google's preferred developer experience. ### Source excerpt Insights on XR, Android CLI, Compose and everything in betweenSource: Photo by Jonathon ReidOverview Attending the Google Android Dev Summit in Mountain View was an exciting experience given it was my first time attending an official event at Google HQ. Compared to massive, spectacle-rich conferences like Google I/O, and Cloud Next, the scale here was noticeably more intimate, allowing direct technical conversations with Google's core platform team and offering unique insight into shifting engineering priorities at Google -- more on that to come. The primary focus centered on enabling agentic coding paradigms, by introducing BYO model capabilities in Android Studio, and expanding command-line tooling via the Android CLI, while also enabling richer and more performant user interfaces with Compose/A2UI improvements. Being there in person was insightful as it provided additional context on upcoming architectural and cultural shifts in the Android ecosystem, revealed by what was said, but more importantly, by what was left unsaid. This was particularly evident with Android Studio: while it received a minor update, it clearly took a back seat to the Android CLI updates and seems to be falling out of favor with Google as the preferred developer experience. Google leaning heavily into adding agentic support. Source: Android Developers YoutubeAndroid CLI & Agentic WorkflowsThe agentic development roundtable to wrap up ADS. Source: Photo by Jonathon Reid The introduction of extended command-line interface (CLI) support for Android represents a major cultural shift towards agentic automation. Built specifically to improve execution performance and minimise context token usage, Android CLI allows autonomous AI agents to execute code development, build tasks, run tests, and confirm they have satisfied the requirements in a verification loop, with the simplicity and performance of the terminal. A key insight emphasised during the sessions was the importance of hand-crafting your ## Agentic Development on Android - Nacho Carrión DevFeed: [Agentic Development on Android - Nacho Carrión]() Original publisher: [Read original article]() Published: 2026-11-04T16:06:24.991412Z Content type: article Language: en Sources: [Touchlab]() Topics: [agentic-coding](), [Android](), [Android Studio](), [Developer experience](), [codex](), [Developer Enablement](), [ML Kit](), [UiAutomator](), [Android XR]() Tags: [a2ui](), [agentic](), [agentic-development](), [agentic-testing](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [android](), [android-development](), [android-skills](), [android-studio](), [antigravity](), [api](), [appfunctions](), [apps](), [code](), [development](), [google](), [tools]() ### AI overview Google's Android tooling is expanding to support agent-assisted development inside familiar workflows. Starting with Android Studio Rabbit 2, developers can select Claude, Codex, Antigravity, or other ACP-compliant agents in the IDE; official plugins also extend support to Claude Code and Codex. Android CLI gives agents emulator access, profiling, and AI-driven Journeys, while Firebase-powered Android Device Streaming enables remote device testing. New Android CLI Skills cover more than 20 Android areas to guide agents toward recommended practices. For app features, AppFunctions (in Beta when written) exposes app functionality to on-device agents, and the A2UI Compose library turns agent-supplied JSON into native Compose interfaces. Journeys use natural-language instructions in XML to perform and verify tests; the author sees potential to complement or eventually replace conventional end-to-end tests, while noting agent testing still needs better approaches. The excerpt also mentions Gemini Nano 4 support in ML Kit, Android Intelligence System, AppState and Navigation 3 updates. ### Source excerpt How the new tools from Google help Android developers to adopt an agentic approach to Android development. ## Android Dev Summit 2026 DevFeed: [Android Dev Summit 2026]() Original publisher: [Read original article]() Author: Sanskar Agrawal Published: 2026-11-04T08:33:50Z Content type: opinion Language: en Sources: [Sanskar's Stuff]() Topics: [Android](), [Android Studio](), [Android Testing]() Tags: [2026](), [a2ui](), [agents](), [android](), [apis](), [cli](), [dev](), [graphics](), [graphics-apis](), [mcp](), [prototype](), [summit]() ### AI overview The author highlights four Android Dev Summit announcements: A2UI, where agents can patch interfaces with seamless transitions; Android MCP, which extends App Functions toward a system-wide MCP solution and raises a boundary question between server and app-level MCP, with offline functions cited as a reason to favor app-level handling; new graphics APIs, especially backdrop APIs, which the author hopes will improve performance for a common UI pattern; and Android CLI updates. The author already uses the CLI for agent-driven preview rendering and app runs, and sees android prototype as a way to speed verification without end-to-end steps. They are still reviewing the announcements. ### Source excerpt Android Dev Summit 2026 wrapped up last week. Some pretty interesting announcements, the team has obviously been busy. Standouts for me: Still sifting... ## Exposing Confetti sessions and bookmarks to AI agents using Android AppFunctions DevFeed: [Exposing Confetti sessions and bookmarks to AI agents using Android AppFunctions]() Original publisher: [Read original article]() Published: 2026-11-01T00:00:00Z Content type: tutorial Language: en Sources: [John O'Reilly]() Topics: [Android](), [AI Bots](), [agentic workflows]() Tags: [a2ui](), [actions](), [adb](), [agent](), [agents](), [ai](), [ai-agents](), [android](), [appfunctions](), [bookmark](), [debug](), [demo](), [dependencies](), [droidcon](), [function](), [functions](), [gemini](), [gradle](), [mcp](), [sessions]() ### AI overview Confetti exposes four Android AppFunctions--`getConference`, `listSessions`, `addBookmark` and `removeBookmark`--so on-device agents can answer questions about the selected conference and manage session bookmarks. The implementation uses AppFunctions `1.0.0-beta01`, KSP-generated service code and schema, and existing repositories; it requires `compileSdk` 37 and `targetSdk` 36 or later. Function and serialized-property KDoc descriptions are passed to the LLM, and the generated service and app metadata must be registered. In the described Gemini Agent Demo test, Gemini found sessions about agents using the conference and session functions, then bookmarked a session that appeared in Confetti. The author had not yet tested with Android MCP. ### Source excerpt AppFunctions allow an Android app to expose functionality that can then be used by AI agents and assistants running on the device. With the recent announcement of Android MCP (which is powered by the AppFunctions API and allows Gemini to make use of functionality in your app) I thought it was a good time to look at adding AppFunctions support to the Confetti conference app. This article will outline the changes made to do that (included in this PR). ## Design and Render AI-Generated Interfaces with Syncfusion A2UI \[Webinar Show Notes\] DevFeed: [Design and Render AI-Generated Interfaces with Syncfusion A2UI \[Webinar Show Notes\]]() Original publisher: [Read original article]() Author: Prabhavathi Kannan Published: 2026-10-28T14:30:00Z Content type: article Language: en Sources: [Syncfusion]() Topics: [a2ui](), [UI/UX Design](), [React](), [User Interfaces](), [agentic workflows]() Tags: [a2ui](), [a2ui-composer](), [agent-to-ui-framework](), [agentic-ui](), [ai](), [ai-generated-interfaces](), [ai-powered-user-interfaces](), [ai-react-web](), [components](), [composer](), [design](), [interfaces](), [learn](), [process](), [react](), [react-ai-applications](), [render](), [syncfusion](), [syncfusion-a2ui](), [syncfusion-a2ui-composer](), [syncfusion-components](), [user](), [using](), [web](), [webinar](), [with]() ### AI overview Syncfusion A2UI separates interface definitions from framework-specific rendering: Composer generates, previews, and refines structured definitions, while an agent returns A2UI messages that a framework runtime validates and renders using supported Syncfusion components. The webinar demonstrates a Python agent connected to a React app for a flight-booking flow, with user actions sent back to advance between search, results, and confirmation screens. Definitions can be generated dynamically or predefined and reused; the source notes reuse can reduce repeated generation and token costs. Composer does not generate framework code directly, and rendering is limited to supported components. A2UI also has renderers for Angular, Blazor, and .NET MAUI. ### Source excerpt Learn how Syncfusion A2UI Composer, AI agents, and React work together to design, process, and render AI-generated user interfaces using supported Syncfusion components. ## Fast, reliable A2UI, with swappable inference formats DevFeed: [Fast, reliable A2UI, with swappable inference formats]() Original publisher: [Read original article]() Author: Vishal Motwani; Google A2UI Team Published: 2026-10-27T21:39:39.465576Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [a2ui](), [AI Chat](), [AI Infrastructure]() Tags: [a2ui](), [agent](), [component](), [dart](), [decision-models](), [express](), [formats](), [generation](), [generative](), [generative-ui](), [inference](), [jev](), [json](), [latency](), [models](), [python](), [sdks](), [standard](), [stream](), [style]() ### AI overview A2UI Express speeds generative UI by letting models produce compact, Python-style statements that the Agent SDK compiles into validated A2UI JSON using the catalog schema. The article reports up to 3x fewer tokens than direct JSON and says Express works well with thinking off; it is supported in the Python, TypeScript, and Dart SDKs, with no frontend renderer or catalog changes required. For screens with layouts defined in advance, TypeSafe's Jev decision model can choose structure through typed questions while a smaller model writes text. In an 11-prompt Apparite example, the component tree appeared in 92-298 ms and screens completed in 0.8-1.7 seconds, but the authors describe this as an early, small-sample result. Controlled generation is limited to layouts covered by its grammar; Express retains A2UI's broader expressivity. ### Source excerpt 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. ## This Week in Open Source for October 23, 2026 DevFeed: [This Week in Open Source for October 23, 2026]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-10-23T18:30:00Z Content type: news Language: en Sources: [Google Open Source Blog]() Topics: [Open Source](), [Maintainers](), [Developer Events](), [gemma](), [Genkit](), [a2ui](), [Digital Public Good]() Tags: [2026](), [22](), [a2ui](), [agent](), [agent-discovery](), [agentic](), [agentic-ai](), [agentic-ai-foundation](), [agentic-commerce](), [agentic-systems](), [agi](), [agntcon](), [ai](), [am](), [events](), [gemma](), [news](), [october](), [open](), [open-source](), [security](), [source](), [this](), [this-week-in-open-source](), [twios]() ### AI overview The excerpt highlights a central open-source sustainability challenge: AI can increase code volume while human review, governance, and security work remains demanding. A study of 294 repositories examined 11 ways to address AI-generated contributions as first-time contributor merge rates fell 18.18%; a separate audit found fewer than 1% of 24,000 commits across 15 critical C projects had explicit machine or agent authorship markers. It also describes Anthropic's opt-in OSS Scanner, which offers free periodic audits with reproducers and suggested patches in isolated Docker environments, and a Gemma-based diarization model that approaches an 8B Llama 3 baseline at half the size. Other featured topics include copyright questions for AI-generated code, self-hosted infrastructure, and voice AI running offline on an ESP32-S3 with 8MB of PSRAM. ### Source excerpt by Daryl Ducharme, Open Source Programs Office A look around the world of open source What actually sustains an open source community when the cost of generating code drops toward zero, while the human labor required to review, govern, and secure it stays just as demanding? Across this week's reads, researchers, legal scholars, and builders examine the tension between automated volume and human stewardship--from public-infrastructure policy and maintainer bandwidth to provenance transparency, copyright law, and compact models running entirely on local hardware. Because software only serves people when human judgment remains in the loop, protecting maintainer agency and keeping project doors open to real newcomers matter more than ever. Upcoming Events 🗓 October 2026 Open Source AI Week 2026 (October 19-24, 2026) -- San Francisco Bay Area, California, USA. Now in its final stretch across the Bay Area alongside PyTorch Conference, this decentralized week of community meetups, workshops, and hackathons--with Google DeepMind and Gemma as Platinum Sponsor--wraps up this weekend with tonight's Open Source AI Awards Night (Friday, October 23, 5:00-10:00 PM PT at Fort Mason, San Francisco) and tomorrow's Google Gemma Hardware Hackathon at AGI House (Saturday, October 24, 10:00 AM-5:00 PM PT in Hillsborough, CA). AGNTCon + MCPCon North America 2026 (October 22-23, 2026) -- San Jose, California, USA. Happening this week, this Linux Foundation and Agentic AI Foundation (AAIF) conference focuses on building and scaling production agentic systems with open standards--featuring keynotes by Google's Rao Surapaneni, Sarah Drasner, and Chi Wang, plus sessions from Googlers Alan Blount (on Agent-to-User Interface [A2UI]), Junjie Bu (on interoperable agent discovery), Amit Handa (on agentic commerce), Dominic Farolino (on Model Context Protocol [MCP] Apps + WebMCP), and Prerna Kakkar & Kurtis Van Gent (on EvalBench). GitHub Universe 2026 (October 28-29, 2026) -- San Francisco, California, U ## Design Agent-Driven UIs Faster with Syncfusion A2UI Composer DevFeed: [Design Agent-Driven UIs Faster with Syncfusion A2UI Composer]() Original publisher: [Read original article]() Author: Siddhartha Devaraj Published: 2026-10-01T15:20:53Z Content type: article Language: en Sources: [Syncfusion]() Topics: [a2ui](), [Design system]() Tags: [a2ui](), [a2ui-composer](), [a2ui-interface-generation](), [agent-driven-ui](), [agent-driven-ui-design](), [agent-workflows](), [ai](), [ai-ai-ui-builder-syncfusion-syncfusion-a2ui-composer-ui-builder-ui-development](), [ai-application-development](), [ai-assisted-interface-design](), [ai-powered-interfaces](), [ai-ui-builder](), [declarative-ui](), [prompt-to-ui](), [react-interface-builder](), [syncfusion](), [syncfusion-a2ui-composer](), [syncfusion-components](), [ui-builder](), [ui-development]() ### AI overview Syncfusion A2UI Composer turns natural-language prompts or templates into structured interface definitions. Developers can preview and refine them with AI-assisted changes or JSON editing, then use the definitions with supported application runtimes. Composer handles design-time authoring; the runtime renders the interface using supported Syncfusion components. ### Source excerpt Explore Syncfusion A2UI Composer to create, preview, and refine agent-driven interfaces using prompts, templates, structured A2UI definitions, and reusable Syncfusion components. ## Introducing Syncfusion A2UI: Build Agent-Driven Interfaces with Reusable UI Components DevFeed: [Introducing Syncfusion A2UI: Build Agent-Driven Interfaces with Reusable UI Components]() Original publisher: [Read original article]() Author: Siddhartha Devaraj Published: 2026-10-01T12:03:06Z Content type: article Language: en Sources: [Syncfusion]() Topics: [a2ui](), [UI components](), [User Interfaces]() Tags: [a2ui](), [a2ui-ai-ai-agents-application-modernization-enterprise-applications-syncfusion-ui-development](), [a2ui-components](), [a2ui-composer](), [agent-driven-user-interfaces](), [agentic-ui](), [ai](), [ai-agents](), [ai-powered-applications](), [ai-powered-user-experiences](), [application-modernization](), [declarative-ui-composition](), [enterprise-ai](), [enterprise-ai-applications](), [enterprise-applications](), [syncfusion](), [ui-development](), [user-interface-composition]() ### AI overview Syncfusion A2UI Preview lets AI agents describe interfaces through structured definitions that applications validate and render using supported reusable components. The article explains the runtime flow, component catalog, and enterprise use cases. A2UI Composer provides an optional visual workflow for designing and refining definitions before integrating them into an application. ### Source excerpt Explore how Syncfusion A2UI helps AI agents describe interactive interfaces that applications render with reusable Syncfusion components. ## What's New in Syncfusion Essential Studio 2026 Volume 3: A2UI, New JavaScript Document Libraries, and More DevFeed: [What's New in Syncfusion Essential Studio 2026 Volume 3: A2UI, New JavaScript Document Libraries, and More]() Original publisher: [Read original article]() Author: Rajeshwari P Published: 2026-09-29T15:57:32Z Content type: release Language: en Sources: [Syncfusion]() Topics: [a2ui](), [chainguard libraries](), [Single-page application (SPA)](), [Web Development]() Tags: [a2ui](), [ai](), [ai-applications](), [ai-desktop-document-processing-library-mobile-syncfusion-web-what-s-new](), [ai-powered-application-development](), [angular](), [blazor](), [blocks](), [collaboration](), [desktop](), [document-chunking-library](), [document-processing](), [document-processing-library](), [dynamic-forms](), [essential-studio](), [essential-studio-2026-volume-3](), [form-builder](), [form-renderer](), [forms](), [javascript](), [javascript-document-libraries](), [javascript-excel-library](), [javascript-powerpoint-library](), [javascript-word-library](), [libraries](), [library](), [mobile](), [net-maui](), [syncfusion](), [web](), [what-s-new](), [whats-new]() ### AI overview Syncfusion Essential Studio 2026 Volume 3 adds tools for AI and document workflows, including a .NET document chunking library for retrieval applications and A2UI for generating interactive interfaces. The release also includes JavaScript libraries for Excel and PowerPoint, dynamic form tools, new UI controls, and document collaboration features. ### Source excerpt Discover what's new in Syncfusion Essential Studio 2026 Volume 3, including AI capabilities, JavaScript libraries for Excel, and PowerPoint, new UI controls, and more. ## From a prompt to native Compose UI: building PocketCommunity with A2UI DevFeed: [From a prompt to native Compose UI: building PocketCommunity with A2UI]() Original publisher: [Read original article]() Author: Akshay Nandwana Published: 2026-09-27T16:54:29Z Content type: tutorial Language: en Sources: [ProAndroidDev]() Topics: [Jetpack Compose](), [a2ui]() Tags: [a2ui](), [ai](), [android](), [android-app-development](), [android-development](), [compose](), [gemini]() ### AI overview This Android engineering walkthrough describes PocketCommunity, an app that uses Gemini to compose interfaces from a restricted component set, a server to validate them, and AndroidX A2UI with native Jetpack Compose to render them. It explains how the app handles interactive state and separates model output, server validation, and rendering responsibilities. ### Source excerpt An Android engineering walkthrough of agent-generated interfaces, component catalogs, stateful interactions, and the boundaries your app still needs to own. Ask an AI assistant to help you prepare for a developer conference. A text response can give you a checklist. An interactive response can let you tick an item, inspect the venue, and ask the assistant to update your plan using what you have already completed. That second interaction is what we built in PocketCommunity, the latest app in the Android Engineers Android + AI Cookbook. The app reads published developer-event listings from DevEarth. Gemini composes an interface from a restricted set of components. A companion server validates that composition, and the AndroidX A2UI renderer displays it using native Jetpack Compose components. The interesting engineering problem starts after the first card appears: how does a tap become agent context, and how do we keep the next generated screen consistent with the user's current state? What actually generates the screen? A2UI is a protocol, not an inference provider. Its renderer does not require a Gemini key. It can render a developer-authored message just as it can render one produced by an agent. PocketCommunity uses Gemini as the producer, so our server needs a Gemini credential. The Android app needs the server URL. These are different responsibilities. In this implementation, Gemini returns: { "text": "Here is a preparation checklist for your event.", "components": [ { "id": "root", "component": "Column", "children": ["heading"] }, { "id": "heading", "component": "Text", "text": "Before you go" } ] } This is a minimal illustrative composition, not a complete event response. The server validates the components and wraps them in A2UI messages. Gemini does not generate Kotlin, and the app does not compile code received from the model. The official Compose A2UI guide describes the separation between the rendering engine and the catalogs that map protocol components ## Rendering AI agent generated UI using Jetpack Compose A2UI and Koog DevFeed: [Rendering AI agent generated UI using Jetpack Compose A2UI and Koog]() Original publisher: [Read original article]() Published: 2026-09-23T23:00:00Z Content type: tutorial Language: en Sources: [John O'Reilly]() Topics: [a2ui](), [AI Agent](), [Jetpack Compose](), [multiplatform](), [Large Language Model](), [Android](), [Kotlin](), [JSON Schema]() Tags: [a2ui](), [ai](), [ai-agent](), [android](), [compose](), [jetpack-compose](), [json](), [json-schema](), [koog](), [kotlin](), [kotlin-multiplatform](), [llms]() ### AI overview This tutorial explains how to render AI-agent-generated user interfaces with A2UI and Jetpack Compose in a Kotlin Multiplatform sample. It covers the Android-only Compose renderer, shared agent code, catalog-based components, A2UI message processing, and Google Gemini integration through Koog. ### Source excerpt A2UI is a protocol that allows an AI agent to describe a user interface as JSON which the client then renders using its own native components (the agent can only make use of components from a "catalog" that the client provides). Google have just released Jetpack Compose support for A2UI (currently 1.0.0-alpha01) and in this article we're going to show how it can be used along with the Koog based AI agent in the ClimateTraceKMP Kotlin Multiplatform sample (I covered the initial Koog exploration in that project in an earlier article). Koog can work with several different LLMs and we're using Google Gemini in this case. ## New Jetpack artifacts include Google's first official A2UI implementation for Compose DevFeed: [New Jetpack artifacts include Google's first official A2UI implementation for Compose]() Original publisher: [Read original article]() Author: CommonsWare Published: 2026-09-23T20:01:08Z Content type: release Language: en Sources: [The CommonsBlog]() Topics: [Jetpack](), [a2ui](), [implementation](), [Google]() Tags: [2026](), [a2ui](), [blog](), [compose](), [google](), [implementation](), [jetpack](), [release](), [sdk](), [september-2026]() ### AI overview This release update covers a new wave of Jetpack artifacts, including Google's first official implementation of A2UI for Compose. It also notes more than 500 new artifact versions and an incomplete artifact release with a missing POM file. ### Source excerpt Here is what we got as new Jetpack artifacts this week! ## AI, Disruption, and Standards DevFeed: [AI, Disruption, and Standards]() Original publisher: [Read original article]() Author: L. Jeffrey Zeldman Published: 2026-09-10T16:12:17Z Content type: opinion Language: en Sources: [Jeffrey Zeldman]() Topics: [Artificial Intelligence](), [ai-governance](), [web-standards](), [Generative AI](), [Model Context Protocol](), [a2ui](), [Web Accessibility (a11y)]() Tags: [a2ui](), [accessibility](), [ai](), [ai-governance](), [appearances](), [architecture](), [best-practices](), [design](), [designers](), [ethics](), [formats](), [generative-ai](), [governance](), [mcp](), [open-source](), [standards](), [ux](), [video](), [web-standards](), [webstandards]() ### AI overview This article introduces a video panel on AI and standards featuring Jeffrey Zeldman and Patrick Neeman. The discussion connects lessons from the web standards movement to AI interfaces, focusing on fragmented user experiences, accessibility, interoperability, provenance, and the role of open-source and community efforts. ### Source excerpt Individually, we have very little power, but together we have a great deal of power. In response to a series of Medium articles by Patrick Neeman--like this one that addressed issues of AI governance, fairness, and standardization--publisher Louis Rosenfeld invited Patrick and me to come on his show and talk about AI and standards. A [...] The post AI, Disruption, and Standards appeared first on Jeffrey Zeldman Presents. ## Android A2UI (AI to UI) Made Simple DevFeed: [Android A2UI (AI to UI) Made Simple]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-09T07:23:57Z Content type: tutorial Language: en Sources: [Mobile App Development Publication]() Topics: [Artificial Intelligence](), [Android](), [Jetpack Compose](), [Mobile](), [Protocol (disambiguation)](), [Google]() Tags: [a2ui](), [ai](), [android](), [android-app-development](), [api](), [artificial-intelligence](), [deprecated](), [google](), [jetpack-compose](), [mobile-app-development](), [software-development](), [user-interfaces]() ### AI overview This tutorial introduces A2UI, a protocol for rendering AI responses as user interfaces, and discusses trying it on Android with Jetpack Compose. It notes that an older Jetpack A2UI library is deprecated and that newer development is ongoing. ### Source excerpt Converting AI Prompt Output Into User Interface in Jetpack Compose Continue reading on Mobile App Development Publication " ## GenUI Beyond Chat: Designing a Grammar Book with Flutter GenUI -- Part 1 DevFeed: [GenUI Beyond Chat: Designing a Grammar Book with Flutter GenUI -- Part 1]() Original publisher: [Read original article]() Author: Cagatay Ulusoy Published: 2026-08-03T15:15:45Z Content type: article Language: en Sources: [Flutter Community]() Topics: [Tutorial](), [Flutter](), [a2ui](), [Internationalization (i18n)](), [Localization (l10n)](), [User Interfaces]() Tags: [a2ui](), [ai](), [firebase](), [flutter](), [gemini](), [generative-ui](), [genuis](), [grammar](), [language-learning](), [personalization]() ### AI overview The article describes a Finnish grammar course built with Flutter GenUI, where learners browse localized curricula and move through interactive pages instead of receiving a long chat response. It separates lesson design into three parts: a curriculum and lesson briefs define what to teach, a system prompt defines how to teach it, and a catalog of registered Flutter components constrains how lessons can appear. Lessons follow Concept -> Mechanism -> Use -> Application, with components for recognition, production, and recall. The model composes A2UI messages from catalog items; Flutter owns rendering, responsiveness, theme, accessibility, and interaction behavior. The author says a roughly 40-component catalog balances pedagogical variety against repeated input-token cost, favoring distinct component roles and letting Flutter handle presentation choices such as table versus cards. Localized curriculum labels are generated from canonical English while preserving Finnish examples and stable lesson IDs. Part 1 covers the design boundaries; lesson batching and Firestore caching are deferred to Part 2. ### Source excerpt Last year on #FlutterFlightPlans live stream, I asked the Flutter team a question that has stayed with me: Are there any use cases for Generative UI other than chat responses? Can you share some inspiration? Seth Ladd answered, "Let's see what is beyond chat together." 🚀 Since that moment, exploring GenUI beyond chat has been my primary focus. https://medium.com/media/7c53d065806a0337fcf3a8db1412ec82/href I shared my first journey with GenUI in an article series: a small GenUI framework built with Gemini structured outputs, and a migration to Flutter GenUI SDK and A2UI surfaces. Then I tried a different kind of use case: a Finnish 🇫🇮 grammar course inside my Finnish it language-learning app. In this feature, the learner browses a curriculum in their own language, opens one small grammar topic, and moves through a sequence of Flutter pages. https://medium.com/media/3ac419e10db257e4f9652bdfb9ad5165/hrefProblem Definition I had four constraints: I do not speak Finnish, and I am not a language teacher. I don't have time to manually plan and structure a grammar book's worth of micro-lessons in every language. I want interactive application UI, not a long Markdown response. Hyper-personalization is crucial. The explanation should begin from the learner's language. A Turkish learner can approach Finnish vowel harmony through Turkish vowel harmony; an English learner needs another bridge since it has no similar concept. Vowel harmony lessons in English and TurkishThe same rule does not feel equally foreign That fourth constraint is more than UI localization. Turkish speakers will find two familiar concepts in Finnish grammar: attaching grammatical markers directly to the ends of nouns, and changing suffix vowels to match the root word. For example, expressing "in" or "at" requires the exact same mental process in both languages: Turkish: ev + -de = evde (in the house) Finnish: talo + -ssa = talossa (in the house) An English speaker has a completely different starting point. ## From Structured Outputs to A2UI Surfaces: Migrating to Flutter GenUI SDK DevFeed: [From Structured Outputs to A2UI Surfaces: Migrating to Flutter GenUI SDK]() Original publisher: [Read original article]() Author: Cagatay Ulusoy Published: 2026-07-08T15:52:59Z Content type: tutorial Language: en Sources: [Flutter Community]() Topics: [Flutter](), [SDK](), [Finite-state machine](), [JSON](), [Firebase](), [Database]() Tags: [a2ui](), [ai](), [firebase](), [flutter](), [gemini](), [generative-ui](), [how-to](), [json](), [sdk](), [state]() ### AI overview A tutorial on migrating a hand-built generative UI feature based on Gemini structured outputs to the official Flutter GenUI SDK. It describes a Finnish language-learning app that generates images on demand and uses a five-step wizard to improve image variety. ### Source excerpt Part 3 of a series "Building Image Assisted Language Learning Practice with Flutter, Firebase and Gemini." Part 1 built the image generation and annotation pipeline. Part 2 built a generative UI framework by hand, using Gemini structured outputs. This part is the migration: same feature, official Flutter GenUI SDK. A year ago, Finnish It was just a side project. This spring, I watched it at the Google Cloud Next developer keynote, and again in the Flutter keynote at Google I/O. It still feels surreal to type that. I am grateful to the Flutter and Firebase teams for the collaboration. This series is my way of giving back. I hope it makes Flutter GenUI SDK click for you the way building this feature made it click for me. Finnish it app featured in Cloud Next and Google IOQuick Recap One of the app's practice exercises is simple: show the learner an image, and they describe it in 🇫🇮 Finnish. The images are generated on demand, not fetched from a fixed image database. In Part 2 the problem was variety. A broad topic like "a native Finnish animal in its habitat" kept producing the same handful of scenes. Turning up temperature did not fix it, because it was never really a sampling problem. An under-specified prompt has a few big probability peaks, and the sampler just jitters around them. The fix was to stop asking the model to "be random" and move the entropy into the UI: a five-step wizard where the user picks the season, the setting, the story, the framing, and the visible details. Gemini designs the questions; the user provides the variety. That worked. The repetition problem was solved. The machine I built by hand To make it work, I built a small finite state machine (FSM) which walked the user through five steps. Each step asked Gemini for question set, constrained by a response schema, and parsed the JSON into a typed object that a widget could render: final response = await model.generateContent( [Content.text(_buildRefinementStateRequest(state, topic, selections ## Demystifying A2UI: How to Make AI Agents "Speak UI" in Your App DevFeed: [Demystifying A2UI: How to Make AI Agents "Speak UI" in Your App]() Original publisher: [Read original article]() Author: Devin Chasanoff Published: 2026-06-26T09:01:02Z Content type: article Language: en Sources: [Angular]() Topics: [a2ui](), [User Interfaces](), [AI for interactive applications](), [Web app](), [software-architecture](), [UI/UX Design]() Tags: [a2a](), [a2ui](), [adk](), [agent](), [agentic](), [agents](), [ai](), [ai-agents](), [angular](), [app](), [client](), [code-execution](), [desktop](), [gemini](), [generative](), [genuis](), [implementation](), [in-app-experiences](), [interactive](), [interfaces](), [llm](), [mobile](), [ui]() ### AI overview A2UI is a protocol for AI agents to describe interactive interfaces in schema-based messages that clients render natively, without executing arbitrary code; rendering remains tied to each client's implementation. On the server, developers define a component catalog, natural-language UI guidance, examples, optional skills, action mappings, tools, and app configuration. On the Angular client, standard components can use the basic catalog; custom components require matching client implementations and server schemas, examples, and registrations. CSS properties can theme basic-catalog views, while custom catalogs may style components independently. The article recommends maintaining a single source of truth for these definitions to keep the agent and client aligned. Its implementation examples focus on Angular and the restaurant_finder and rizzcharts samples; it does not give a complete setup walkthrough in the supplied text. ### Source excerpt I've spent a fair amount of time experimenting with A2UI, a protocol for building agentic interfaces. Why? Quite simply, because I think the idea of dynamically generating user interfaces is awesome, and despite the fact that this is a relatively new protocol (currently 1.0 RC), I wanted to answer a fundamental question: "How do I incorporate A2UI into my own app?" If you're new to working with generative UIs, it may be challenging to wrap your head around the core concepts. This post is my mental model for how everything works from a developer's implementation perspective so that you can start using it in your own applications. I'm incredibly excited about the future of web apps. So much so that last November, I hosted a livestream called Exploring the future of web apps during which I built my own framework for generating agentic web apps with Angular. A Quick Side Note: While the framework I built for that livestream isn't nearly as flexible or extensible (to use with other web frameworks, for example) as A2UI, the architectural patterns are very similar. If you're still struggling to understand the high-level concepts after reading the A2UI docs and this blog post, give that livestream a watch. The shared concepts will help make it click.What is A2UI? A2UI enables AI agents to generate rich, interactive user interfaces that render natively across web, mobile, and desktop -- without executing arbitrary code. This is exciting to me because A2UI enables: Seamless in-app experiences: Interfaces that adapt dynamically to user intent. Portable UIs: Views that can appear virtually anywhere, serving as a vital part of Gemini web app experiences. Protection: No arbitrary code execution. But to understand A2UI, you first have to understand what it isn't. It's not a framework; it's a protocol. The implications of this are massive. Because it's a protocol, using GenUI doesn't lock you into a single stack. It defines how an LLM agent and a client application communicate about ## How we built a Flutter-powered AI coffee shop DevFeed: [How we built a Flutter-powered AI coffee shop]() Original publisher: [Read original article]() Author: Craig Labenz Published: 2026-06-22T15:16:53Z Content type: article Language: en Sources: [Flutter]() Topics: [Flutter](), [generative-ai-tools](), [User interface design](), [implementation](), [CI/CD]() Tags: [a2ui](), [ai](), [cloud-next](), [coffee](), [coffee-shop](), [demo](), [firebase](), [flutter](), [gemini](), [genuis](), [google-i-o]() ### AI overview GenLatte combined Flutter, Firebase, Gemini, and Nano Banana to turn attendees' short "happy place" prompts into customized latte art. Gemini expanded prompts into four image requests, and Nano Banana generated options; users could refine an image by answering four tailored questions, with Gemini selecting from a small catalog of branded UI widgets. The Flutter app served kiosk, barista, moderator, queue, and recent-orders screens, backed by Firebase functions and Firestore. For safety, the system moderated text with Gemini Flash, relied on Nano Banana's safeguards for image generation, and required human image approval; Firebase custom claims and strict security rules controlled privileged screens and database access. The team reports serving 1,200 lattes at Cloud Next and 1,800 at Google I/O. The code is described as unmaintained and for inspiration only. ### Source excerpt Dash sits on a coffee shop's checkout counter drinking a latte with the Flutter logo printed on top If you start giving away coffee, people will show up. The Flutter team had been trying for a while to find a way to put this principle to work with a demo that started with a Flutter app and ended with a hot, tasty beverage. The problem was how to make it more interesting, more personal than just taking orders and delivering. Coffee's cool, but it's not The One Thing You Definitely Remember from Google I/O cool. So what changed? Two new bits of tech arrived on scene. One is Nano Banana, with its ability to quickly generate customized images. The second is GenUI, with its ability to generate UX dynamically at runtime and offer attendees personally tailored questions about how they'd like to modify those images. Put the two together with Flutter and you can offer folks the chance to order coffee with an image of their happy place printed on top with an inkjet printer. Now that's pretty memorable. All we had to do was execute. Twice, as it turned out -- the demo was called "GenLatte" at Cloud Next and "Antigravity Coffee Co." at Google I/O. For simplicity and to save a few tokens for the agent summarizing this article for you, the name "GenLatte" is used below. We built a coffee shop Realizing a vision as ambitious as GenLatte involves a lot of people. Like, a lot of people. There are teams to spec out the physical space and decide how "customers" [1] will flow. There are branding and concept teams to create the aesthetic. There are of course construction teams to go to the hardware store and buy lumber. Eventually, there's also the software engineers slapping code together to build the various apps, which is where I happened to come in. This blog post will only outline the software, but that is not to minimize the incredible work that went into the many other layers required for GenLatte to eventually open its doors. [1] Are they still customers if you don't charge anyth ## How we used Gemini to build Google I/O 2026 DevFeed: [How we used Gemini to build Google I/O 2026]() Original publisher: [Read original article]() Author: Marvin Chow Published: 2026-06-01T16:00:00Z Content type: article Language: en Sources: [Google Labs]() Topics: [generative-ai-tools](), [google-antigravity](), [Firebase](), [UI/UX Design](), [API Business Models]() Tags: [a2ui](), [ai](), [ai-products](), [ai-tools](), [antigravity](), [backend](), [cloud-functions](), [developer-tools](), [gemini](), [gemini-models](), [google-i-o](), [google-labs](), [image](), [none]() ### AI overview Google describes how its team used Gemini, Nano Banana, and other AI tools to create Google I/O 2026 films, visual identity, interactive experiences, games, and attendee apps. Examples include a custom Google AI Studio tool for testing generated animation frames, a jellyfish-tracking music experiment, and Flutter and Firebase components for a generative coffee-ordering app. ### Source excerpt A collage of I/O-related images, including the Antigravity Coffee Co. pop-up, a colorful jellyfish and a still from the Timmy TPU video. The word AI repeats three times on the left of the image, and there are colorful icons, including a sparkle, as well. ## Meet the A2Family DevFeed: [Meet the A2Family]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-04-23T18:30:00Z Content type: article Language: en Sources: [Google Open Source Blog]() Topics: [A2A protocol](), [Multi Agent Systems](), [a2ui](), [agent to agent communication](), [google-adk](), [agentic workflows](), [linux foundation]() Tags: [a2a](), [a2a-protocol](), [a2ui](), [adk](), [ai-agents](), [ap2](), [collaboration](), [communication](), [frameworks](), [go](), [google](), [interoperability](), [linux-foundation](), [mcp](), [open-source](), [open-standard](), [scale](), [security](), [skills]() ### AI overview The A2Family presents open tools for connecting AI agents and commerce workflows. A2A is an open standard for agents built with different frameworks or by different vendors to delegate tasks, exchange information, and coordinate; it is described as donated to the Linux Foundation. The article distinguishes MCP tools, suited to quick deterministic resources or actions, from A2A collaboration with another agent. A2UI lets agents send interactive interfaces using pre-approved catalog components without executing arbitrary code; MCP Apps instead deliver widgets in iframes orchestrated through MCP events and tool calls, while AG UI connects agents to front ends. AP2 adds payment authorization and accountability mechanisms using signed, tamper-evident credentials. UCP standardizes the broader shopping journey and supports A2A and AP2, while retailers retain control of their rules and customer relationships. ADK, described as related but not part of the A2Family, is a framework for building and deploying agents in Python, TypeScript, Go, or Java. ### Source excerpt by Daryl Ducharme, Google Open Source & Alan Blount, Cloud AI At Google, we know that building on open source gives teams the freedom and flexibility to use meaningful technologies faster. Openness drives innovation and security, and it is core to our mission. As we look toward the future of computing, we want to ensure that developers across all open source communities have the foundational tools they need to build secure and collaborative AI systems. That is why we are excited for you to get to know the "A2Family"--a suite of open source protocols and tools designed to help you build, connect, and scale your AI agents. A2A: The cornerstone of agent interoperability The Agent2Agent (A2A) Protocol is an open standard designed to enable seamless communication and collaboration between AI agents. It provides the definitive common language for agent interoperability in a world where agents are built using diverse frameworks and by different vendors. Originally developed by Google, A2A has now been donated to the Linux Foundation. As a famous open source aphorism reminds us: "If you want to go fast, go alone. If you want to go far, go together." A2A brings this collaborative philosophy to AI, allowing agents to delegate sub-tasks, exchange information, and coordinate actions to solve complex problems that a single agent cannot. MCP & Skills: Agents need tools and skills Since day one A2A has loved MCP, and we love skills too ♥. Agents discover, negotiate, converse, make plans, adapt when those plans don't work out - that's a different interaction pattern than a tool and that's what A2A was built for. But for your agents to function, they need access to tools, and instructions on how to use those tools safely and securely. While MCP and A2A might not be from the same origin story, they are a family that works better together. When you're not sure - if it's a quick deterministic resource or action, it's a tool, but if you may end up with a conversation, it's an agent. Ano