# 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. ## 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](), [MCP](), [a2ui](), [Accessibility]() 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 - Medium]() 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 " ## 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 - Medium]() Topics: [Flutter](), [SDKs](), [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: tutorial Language: en Sources: [Angular]() Topics: [Protocol (disambiguation)](), [ui](), [Artificial Intelligence](), [Angular](), [Large Language Model](), [Web](), [web applications](), [Architecture & Design](), [Front end]() Tags: [a2a](), [a2ui](), [adk](), [agentic](), [agents](), [ai](), [ai-agents](), [angular](), [architecture](), [frontend](), [genuis](), [how-to](), [in-app-experiences](), [llm](), [protocol](), [ui](), [web](), [web-app]() ### AI overview This tutorial explains A2UI, a protocol that lets AI agents generate rich, interactive user interfaces for web, mobile, and desktop applications without executing arbitrary code. It presents a developer-oriented mental model covering the protocol's architecture, client-server communication, and rendering flexibility. ### 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 - Medium]() Topics: [Flutter](), [Artificial Intelligence](), [Firebase](), [monorepo](), [Cloud](), [Deployment](), [Web](), [Android](), [iOS]() Tags: [a2ui](), [ai](), [android](), [app](), [apps](), [cloud](), [code](), [deployment](), [firebase](), [flutter](), [gemini](), [genuis](), [go](), [google](), [hosting](), [ios](), [monorepo](), [web]() ### AI overview The Flutter team describes building GenLatte, a coffee-shop demo that combined a Flutter app with Nano Banana for customized images and GenUI for dynamically generated, personalized questions. The system let visitors order coffee topped with an image of their happy place, and the demo was presented at Cloud Next and Google I/O. ### 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 ## Google introduces the A2Family of open source protocols and tools for AI agents DevFeed: [Google introduces the A2Family of open source protocols and tools for AI agents]() 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](), [Artificial Intelligence](), [interoperability](), [Model Context Protocol](), [Open Source](), [linux foundation]() Tags: [a2a](), [a2a-protocol](), [a2ui](), [adk](), [agent2agent](), [agents](), [ai](), [ai-agents](), [ap2](), [interoperability](), [linux-foundation](), [open-source]() ### AI overview Google presents the A2Family, a suite of open source protocols and tools for building, connecting, and scaling AI agents. The article describes A2A for agent interoperability, its relationship with MCP and skills, and A2UI for rendering interactive interfaces without executing arbitrary code. ### 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