# A2A protocol Published articles for A2A protocol. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Adapting open source practices to an AI-first world: A retrospective on 2025 DevFeed: [Adapting open source practices to an AI-first world: A retrospective on 2025]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-08-03T18:30:00Z Content type: article Language: en Sources: [Google Open Source Blog]() Topics: [Open Source](), [Google](), [A2A protocol](), [interoperability](), [GitHub](), [linux foundation](), [Protocol (disambiguation)](), [LLVM](), [Rust](), [vllm](), [gRPC](), [Kubernetes]() Tags: [2025](), [a2a](), [a2a-protocol](), [agents](), [ai](), [ai-agents](), [github](), [google-open-source](), [interoperability](), [kubernetes](), [linux](), [linux-foundation](), [open-source](), [report-card](), [retrospective](), [rust](), [security](), [vllm]() ### AI overview Google reviews Alphabet's 2025 open source activity, including employee and external contributions, collaboration on the Agent2Agent protocol, and the maintenance and launch of public repositories and tools. ### Source excerpt by Sophia Vargas, Google Open Source Even as AI adoption accelerates and transforms the global technology landscape, open source remains foundational to how Alphabet builds, uses, and collaborates on products for billions of users. Our commitment to open source remains broad and consistent, including sharing our work year-over-year, and reflecting on what we've learned. In 2025: Roughly 10% of Alphabet's full-time workforce actively contributed to open source projects. This contribution ratio has remained steady over the past five years, scaling to match our growth. These open source contributions are not just solely focused on Google. Our top projects by unique contributors at Alphabet include community-led projects such as LLVM, vLLM, Envoy, and Rust, as well as Google-initiated projects like Kubernetes, Apache Beam, and gRPC. In addition, Alphabet projects received commits from more than 20,000 non-Alphabet affiliated user accounts. Working together on emerging standards Open source communities continue to provide vital collaborative spaces to define emerging standards, ensuring the interoperability and extensibility for the next generation of technologies. In 2025, we worked with more than 50 partners on the Agent2Agent (A2A) protocol to enable AI agents to communicate with each other, securely exchange information, and coordinate actions on top of various enterprise platforms and applications. Within weeks of our initial announcement, Google donated the A2A project to the Linux Foundation as part of our long-standing commitment to develop "open, collaborative ecosystem - offering greater autonomy and multiplying productivity." Launching tools with transparency Open source licenses provide a framework for anyone to explore, test, fork and expand on our technologies. Over the last 15 years, Google has created more than 15,000 public repositories on GitHub. Today, Google continues to maintain more than 5,000 public repositories on GitHub, and more than 1,500 publi ## Multi Agent Systems for Platform Engineering, Patterns and Architecture DevFeed: [Multi Agent Systems for Platform Engineering, Patterns and Architecture]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-07-26T16:16:42Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [Platform Engineering](), [distributed-systems](), [systems](), [Model Context Protocol](), [Provisioning]() Tags: [a2a-protocol](), [agent](), [agent-orchestration](), [agent-to-agent-communication](), [ai-agents](), [architecture](), [audit-trail](), [automation](), [devops](), [distributed-systems](), [hierarchical-agent-systems](), [internal-developer-platform-automation](), [kubernetes](), [langgraph](), [langgraph-multi-agent](), [mcp](), [mcp-for-platform-engineering](), [multi-agent](), [multi-agent-architecture-patterns](), [multi-agent-systems](), [multi-agent-systems-for-platform-engineering](), [platform-engineering](), [platform-engineering-automation](), [provisioning](), [supervisor-agent-pattern]() ### AI overview A practical guide to designing multi-agent systems for platform engineering. It compares supervisor, pipeline, fan-out, hierarchical, and swarm patterns; explains how agent contracts, identity boundaries, audit trails, and verification address common failure modes; and distinguishes MCP from A2A. ### Source excerpt Studies of real agent systems put failure rates between 41 and 86 percent, and almost none of it is the model's fault. Here are the patterns that hold up for platform work, and the discipline that decides whether yours does. ## A2A Protocol - Deep Dive DevFeed: [A2A Protocol - Deep Dive]() Original publisher: [Read original article]() Author: Eric Roby Published: 2026-06-26T13:43:17Z Content type: article Language: en Sources: [System Design Newsletter]() Topics: [Protocol (disambiguation)](), [Artificial Intelligence]() Tags: [a2a](), [a2a-protocol](), [agents](), [ai](), [ai-agents](), [deep-dive](), [protocol]() ### AI overview A deep dive into the A2A Protocol and how AI agents collaborate to solve complex tasks. ### Source excerpt #158: How AI agents collaborate to solve complex tasks ## A2A in Java: hands on Embabel DevFeed: [A2A in Java: hands on Embabel]() Original publisher: [Read original article]() Author: Aneshka Goyal Published: 2026-06-16T08:44:44Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Embabel](), [A2A protocol](), [Java](), [Spring AI](), [Agent Framework](), [interoperability](), [Artificial Intelligence](), [Large Language Model]() Tags: [a2a](), [a2a-protocol](), [agent-framework](), [ai](), [embabel](), [interoperability](), [java](), [large-language-models-llms](), [software-engineering](), [spring-ai]() ### AI overview This hands-on article introduces A2A in Java and explores Embabel, a Java agent framework built on Spring AI. It explains how A2A enables interoperability among agents across languages, frameworks, vendors, and platforms, and describes Embabel's use of goal-oriented action planning with existing Java domain logic and structured code. ### Source excerpt Part 1: What is Embabel? A2A, or Agent-to-Agent Protocol, is an open standard (now under the Linux Foundation) that provides a common language for diverse AI agents to communicate, collaborate, and share tasks, enabling interoperability across different frameworks, vendors, and platforms. You can read more about A2A here. A2A makes agents framework and tech stack agnostic, which means agents developed in Python will be able to connect to other agents in Java or any other programming language of choice (given we have A2A protocol compliance). We currently have ADK (Python) and other Python specific frameworks that simplify the development of A2A compliant agents (as you'll read in this blog). This initiative explores creating a comparable Java-specific framework to enable building A2A-compliant agentic applications in Java. Brief about Spring AI Before learning about Embabel, it's important to discuss and know about Spring AI which provides the foundation for Embabel framework. The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. This project draws inspiration from notable Python projects, such as LangChain and LlamaIndex, but Spring AI isn't a direct port of those projects. The project was founded with the belief that the next wave of generative AI applications will not only be for Python developers but ubiquitous across many programming languages. Spring AI can be used without Embabel (or any other high level framework like ADK java) to create agents and agentic applications. For more info around how to use Spring AI, check out Part 1 and Part 2. What is Embabel? Embabel is an agent framework built on Spring AI, designed to integrate Large Language Models (LLMs) with existing domain logic and structured code in Java applications. It utilizes a Goal-Oriented Action Planning (GOAP) mechanism to allow agents to dynamically determine the sequence of actions needed ## 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 ## A year of open collaboration: Celebrating the anniversary of A2A DevFeed: [A year of open collaboration: Celebrating the anniversary of A2A]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-04-16T20:55:00Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [A2A protocol](), [Artificial Intelligence](), [Google](), [interoperability](), [Open Source](), [linux foundation](), [Security](), [Load Balancing]() Tags: [a2a](), [a2a-protocol](), [a2april](), [agent2agent](), [agents](), [ai](), [ai-agents](), [amazon](), [amazon-web-services](), [cisco](), [cryptographic](), [interoperability](), [linux-foundation](), [load-balancing](), [microsoft](), [open-source](), [sap](), [security](), [specifications](), [standard]() ### AI overview Google reflects on the first year of the Agent2Agent (A2A) protocol, including its donation to the Linux Foundation, the growth of its supporting ecosystem, and the release of A2A Protocol v1.0. The article highlights security, enterprise architecture, and interoperability improvements for multi-agent workflows. ### Source excerpt by Patricia Cruz, Google Open Source One year ago, on April 9th, 2025 Google announced the Agent2Agent(A2A) protocol. We saw the need for a "common language" that allows AI agents built on different frameworks to collaborate well across diverse systems. Then, on June 23, 2025 at the Open Source Summit North America in Denver, Mike Smith stood on stage to share a pivotal moment for the future of AI interoperability when Google officially donated the A2A protocol to the Linux Foundation, establishing it as a vendor-neutral, community-governed standard. This move was driven by a core belief: for AI agents to truly transform how we work and live, they must be able to communicate across framework boundaries and organizational silos without being locked into a single provider's ecosystem. By placing A2A under the neutral stewardship of the Linux Foundation, we opened the doors for the entire industry to build, contribute, and innovate together. A Foundation of Partners The formation of the A2A Project was made possible through the support of our founding members, including Amazon Web Services, Cisco, Microsoft, Salesforce, SAP, and ServiceNow. Over the past twelve months, this coalition has grown, with over 100 technology companies now supporting the project. From Prototype to Production The momentum since the donation has been remarkable. What began as a Google-led initiative has evolved into critical infrastructure for horizontal, peer-to-peer collaboration. Just one month ago, in March, the project reached a major milestone with the release of A2A Protocol v1.0, the first stable, fully production-ready version of the standard. Key achievements from the community this year include: Enhanced Security: The implementation of Signed Agent Cards for cryptographic identity verification, ensuring trust in multi-agent workflows. Web-Aligned Architecture: Refined specifications that support familiar load-balancing and security patterns for enterprise-scale deployments. Ecosystem ## Evaluating the Value of llms.txt for AI Retrieval DevFeed: [Evaluating the Value of llms.txt for AI Retrieval]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2025-05-09T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [LLMs](), [web-standards](), [AI Trends]() Tags: [a2a-protocol](), [ai-trends](), [anthropic](), [generative-engine-optimization](), [google](), [llms]() ### AI overview The article evaluates whether llms.txt provides practical value for AI-driven discovery and retrieval. It argues that structured, simplified text files can help large language models process web content more efficiently, citing reported adoption and observations from Vercel, Google, Anthropic, and Windsurf. ### Source excerpt If you've been following AI trends, you've probably heard of llms.txt.