# adk Published articles for adk. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From Project Description to Funding Opportunity DevFeed: [From Project Description to Funding Opportunity]() Original publisher: [Read original article]() Author: Gabriel Preda Published: 2026-08-31T05:03:07Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [Artificial Intelligence](), [Google AI](), [Streamlit](), [Cloud](), [PostgreSQL](), [postgresql clusters]() Tags: [adk](), [ai](), [building](), [cloud](), [developer](), [gemini](), [google](), [google-adk](), [google-cloud-sql](), [pgvector](), [postgresql](), [search](), [streamlit]() ### AI overview This article describes GrantMatch AI, a Streamlit application that matches project descriptions with potentially relevant grant and funding opportunities. It explains how hybrid keyword and embedding search, Gemini, ADK, Cloud SQL, PostgreSQL, pgvector, and Streamlit are used to connect related concepts despite different wording. ### Source excerpt Building GrantMatch AI with hybrid keyword and embedding search using Gemini, ADK, Cloud SQL, PostgreSQL, pgvector, and Streamlit Continue reading on Google Developer Experts " ## Coding Challenge #129 - Coding Challenges Coach DevFeed: [Coding Challenge #129 - Coding Challenges Coach]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-07-31T08:02:05Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Code Challenge](), [AI Agent](), [Artificial Intelligence](), [Google](), [Google Cloud Platform (GCP)](), [Agent Framework](), [Security](), [Command-line interface]() Tags: [adk](), [agents](), [ai-agents](), [cli](), [coding](), [gemini](), [google-cloud](), [how-to](), [security]() ### AI overview Coding Challenge #129 guides developers through building an AI coaching agent for Coding Challenges. The project uses Google's Agent Development Kit and Gemini Enterprise Agent Platform, progressing from a local prototype to a production service on Google Cloud with state management, cost tracking, observability, and security auditing. ### Source excerpt This challenge is to build your own AI coach for Coding Challenges, learning how to build and deploy production grade AI agents. ## 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 ## 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 ## Improve AI output with continuous improvement DevFeed: [Improve AI output with continuous improvement]() Original publisher: [Read original article]() Author: Alexander Nohe; Dan Gruhl Published: 2026-01-13T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Artificial Intelligence](), [Chain-of-thought]() Tags: [adk](), [agentic](), [ai](), [automated](), [firebase](), [gemini](), [prompting](), [python](), [rewrite](), [verify]() ### AI overview A tutorial explains continuous improvement, a prompting strategy in which one AI generates an output and another AI critiques it before the first AI revises the result. It demonstrates the approach with ADK and LoopAgent for designing gym workouts. ### Source excerpt Learn how to improve your AI outputs to a more reliable output through continuous improvement. ## How to Write an Agent DevFeed: [How to Write an Agent]() Original publisher: [Read original article]() Author: Simone Margaritelli Published: 2025-03-13T01:36:08Z Content type: tutorial Language: en Sources: [evilsocket]() Topics: [AI Development](), [function calling](), [LLMs]() Tags: [adk](), [agent](), [agent-development-kit](), [agent-evals](), [ai](), [autonomous-agents](), [blog](), [cybersecurity](), [evals](), [evaluations](), [function-calling](), [how-to](), [howto](), [llm](), [nerve](), [nerve-adk](), [project-release](), [tool-use]() ### AI overview This tutorial explains how software agents use models to select tools in a loop, and how function calling lets language models invoke tools. It introduces Nerve as a project intended to simplify implementing an agent and discusses executing tool calls and returning their outputs to the model. ### Source excerpt Hello friends. This blog post was supposed to be the second part of this re