# 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. ## Aegis OS: A Transactional Multi-Tenant Operating System Runtime for Eliminating Authorization Fatigue in Autonomous Coding Agents DevFeed: [Aegis OS: A Transactional Multi-Tenant Operating System Runtime for Eliminating Authorization Fatigue in Autonomous Coding Agents]() Original publisher: [Read original article]() Author: Hui Zhao (Heurion Research) Published: 2026-11-09T05:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Software Engineering]() Topics: [Multi Agent Systems](), [Authorization](), [Kernel](), [Agent Harness](), [A2A protocol](), [agentic workflows](), [tenant data protection](), [google-antigravity]() Tags: [a2a](), [a2a-protocol](), [agent2agent](), [agents](), [anthropic](), [anthropic-claude](), [antigravity](), [api](), [arxiv](), [attack](), [attacks](), [authorization](), [autonomous-coding](), [autonomy](), [boundary](), [bridge](), [coding-agents](), [cs-cr](), [cs-ma](), [cs-os](), [cs-se](), [operating-system](), [os](), [runtime]() ### AI overview Aegis OS moves authorization from individual tool calls to transactional workspace boundaries to reduce prompts while isolating autonomous coding agents. Its Rust runtime uses macOS Seatbelt or Linux Bubblewrap sandboxes, copy-on-write shadow workspaces, an identity-pinned HTTP egress proxy for scoped secret injection and outbound DLP, and an A2A bus with content-addressable storage. The abstract reports 94.19%-99.88% fewer prompts in 1,500 controlled calls and 90.7%-97.9% fewer across 28,087 calls in 52 sessions; attack success fell from 100% to 0% across 80 attacks on both platforms. It also reports 6.3 ms APFS and 15.6 ms btrfs cloning for a 1,000-file repository and detection of all 10 tested merge conflicts. These are claims from the supplied abstract; methods and broader limitations are not described. ### Source excerpt arXiv:2611.08516v1 Announce Type: cross Abstract: Autonomous coding agents such as Google Antigravity (agy), Anthropic Claude Code (claude), and OpenAI Codex CLI (codex) execute multi-step software engineering workflows on workstations and cloud hosts. Existing agent environments face a Security-Autonomy Dilemma: operators must choose between per-action confirmation prompts, which interrupt workflows on 45%-57% of tool calls and induce authorization fatigue, or unconfined execution (--yolo / --dangerously-skip-permissions), which exposes host files, SSH/cloud credentials, and API tokens to prompt injection and hallucination risks. Furthermore, concurrent agents editing a shared working tree clobber one another's changes, while passing large artifacts inline through LLM context windows incurs high token overhead. We present Aegis OS (aegis-os), a transactional, multi-tenant Agent Operating System Runtime in Rust exposed via the Model Context Protocol (MCP) and Google Agent2Agent (A2A) protocol. Aegis OS shifts the authorization boundary from individual tool calls to transactional workspace boundaries. Each agent runs inside a kernel-enforced OS sandbox (macOS Seatbelt and Linux Bubblewrap with default-deny --tmpfs $HOME isolation and a Unix-socket egress bridge) backed by a Copy-on-Write (CoW) shadow workspace (APFS clonefile; single-open Linux FICLONE reflinks), an identity-pinned HTTP Egress Proxy with L7 method/path-scoped secret injection and outbound DLP, and an A2A IPC bus with Content-Addressable Storage (CAS). Across 1,500 controlled calls and 28,087 real calls across 52 coding-agent sessions, Aegis OS reduces prompts by 94.19%-99.88% (controlled) and 90.7%-97.9% (real sessions, 1.12% IPR), lowers attack success rate across 80 attacks in eight categories from 100.0% to 0.0% on macOS and Linux, clones 1,000-file (78.1 MB) repos in 6.3 ms on APFS and 15.6 ms on btrfs, and catches 10/10 syntactic and semantic merge conflicts. ## Why agent-to-agent chains need per-hop delegated tokens DevFeed: [Why agent-to-agent chains need per-hop delegated tokens]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-10-29T00:00:00Z Content type: article Language: en Sources: [WorkOS]() Topics: [Secure token management](), [Multi Agent Systems](), [agent to agent communication](), [Secrets Management]() Tags: [a2a-protocol](), [agent](), [authorization](), [blast-radius](), [credentials](), [delegation](), [exchange](), [tokens]() ### AI overview Shared long-lived credentials let a manipulated agent use authority across an entire chain. The post recommends issuing each hop a short-lived, narrowly scoped token for its specific audience, carrying upstream identity, and obtaining downstream tokens through exchange rather than passing credentials through. This supports least privilege, attribution, targeted revocation, and boundary policies. It does not prevent prompt injection or SSRF; redirect and IP-range controls remain necessary. The MCP authorization spec requires audience validation and forbids token passthrough but does not prescribe token exchange. WorkOS describes distinct mechanisms: AuthKit issues resource-bound tokens; Agent Auth re-derives permissions at each hop, with tokens lasting at most one hour and revocation cascading to child sessions. Agent Auth's `act` claim identifies the delegating user, not every intermediate hop, and its chaining uses WorkOS's minting API rather than RFC 8693 exchange. Relay keeps third-party provider credentials outside the agent by attaching them at the API boundary. ### Source excerpt Shared credentials turn one manipulated agent into a compromised chain. Narrow, audience-bound tokens at every hop shrink the blast radius. ## SAP wants Joule to run business workflows, not just answer questions DevFeed: [SAP wants Joule to run business workflows, not just answer questions]() Original publisher: [Read original article]() Author: Taryn Plumb Published: 2026-10-27T08:05:00Z Content type: news Language: en Sources: [CIO]() Topics: [agentic workflows](), [Multi Agent Systems](), [AI Infrastructure](), [control-plane](), [A2A protocol]() Tags: [a2a](), [a2a-protocol](), [agent](), [agent2agent](), [agentic](), [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-assistants](), [ai-platform](), [apps](), [artificial-intelligence](), [artificial-intelligence-enterprise-applications-erp-systems-sap-vendors-and-providers](), [build](), [building](), [business](), [capabilities](), [edition-us](), [enterprise-applications](), [erp-systems](), [joule](), [language-en](), [run](), [sap](), [storytype-news](), [vendors-and-providers](), [workflows]() ### AI overview SAP is expanding Joule from a conversational assistant into a platform for building and running multi-step business workflows. Joule Studio will integrate natively with Joule Work so users can describe an intent and have the system coordinate agents, apps, and data; workflows can be saved as reusable skills and shared. Agents are intended to connect to external systems through MCP and A2A, while builders gain shared, role-based environments, sandboxes, and offline evaluations. SAP also describes a Knowledge Graph drawing on SAP and non-SAP data, a governed gateway for third-party agents, and an Enterprise AI Gateway that centralizes authentication, policy enforcement, routing, monitoring, and auditing. Joule Work and Studio features are rolling out in phases; general availability for the gateway and new Knowledge Graph capabilities is planned for Q1 2027. The article also reports separate data, RISE with SAP, and SAP-RPT-2.0 announcements. ### Source excerpt SAP says it is ushering in a new era of agentic AI, one where agents don't just learn a business, but help run it. This week at its TechEd conference, the company is announcing new tools in its SAP Business AI Platform that will extend AI agents' capabilities, give them richer, more enterprise-specific data and context, and beef up governance controls so they can be rolled out safely across enterprise workflows. "There used to be a time where AI assistants or interfaces could do a couple queries, and you had to get the query more or less right," SAP chief AI officer Jonathan von Rüden told CIO. "And then if you asked three, four questions, eventually it would say, 'I can't go that far.' Now that constraint isn't really there anymore." New orchestration layer SAP has introduced new capabilities in its AI-driven workspace Joule Work and its AI no-code/pro-code agent building environment, Joule Studio. Joule Studio will now be natively integrated with Joule Work, giving users a single build layer. They will be able to describe their intent, and the system will orchestrate the right agents, apps, and data. Joule Work is built on the SAP Business AI Platform, which provides context, data, and governance. "Effectively, business users get a coding agent into their hands, but they don't know it is one, so they come up with all kinds of very, very interesting use cases," von Rüden said. SAP has put a good deal of R&D effort into graduating from conversations to a generative UI to "step-by-step" replace classic UIs, he noted. "You don't need those anymore, you can have them tailor made per person." A new orchestration layer in Joule Work will support more complex, multi-step workflows. Beyond working across SAP environments, agents will be able to plug into external systems, data, and tools via Modern Context Protocol (MCP) and Agent2Agent (A2A) Protocol. Further, Joule Studio will be more tightly integrated with SAP Business Data Cloud so that builders can access various dat ## A2ABreak: Systematic Security Analysis of the A2A Protocol DevFeed: [A2ABreak: Systematic Security Analysis of the A2A Protocol]() Original publisher: [Read original article]() Author: Alireza Lotfi, Mirza Masfiqur Rahman, Imtiaz Karim, Elisa Bertino Published: 2026-10-27T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Software Engineering]() Topics: [A2A protocol](), [Multi Agent Systems](), [Finite-state machine](), [Security]() Tags: [a2a](), [a2a-protocol](), [agent2agent](), [agents](), [ai](), [ai-agents](), [analysis](), [autonomous](), [autonomous-ai-agents](), [communication](), [compliance](), [context](), [credential-harvesting](), [cs-cr](), [cs-se](), [data-exfiltration](), [delegation](), [formal](), [formalized](), [framework](), [llm](), [security]() ### AI overview A2ABreak reports 11 protocol-level vulnerabilities in A2A that a specification-compliant adversary can exploit without implementation flaws. The framework uses LLM-assisted extraction of a verified finite-state machine from the natural-language specification (37 states, 76 transitions, and 929 formalized statements), then applies adversarial verification. Reported examples include cross-client context injection through unprotected context identifiers, credential harvesting after identity loss in delegation chains, and data exfiltration via agents making unattested capability claims. Against manual expert review, it achieves 73.3% precision and 84.6% F1; a zero-shot LLM baseline on the same specification yielded no confirmed findings. These are abstract-level claims; the supplied text does not detail mitigations or individual vulnerability analyses. ### Source excerpt arXiv:2609.10871v3 Announce Type: replace Abstract: The Agent2Agent (A2A) protocol, now governed by the Linux Foundation, is an open standard that enables autonomous AI agents to discover, authenticate with, and delegate tasks to one another across organizational boundaries. Designed to complement the Model Context Protocol (MCP) for tool integration, A2A is rapidly emerging as the horizontal communication layer of the multi-agent ecosystem. Yet the protocol's security has received no systematic analysis. This paper presents A2ABreak, the first rigorous systematic security analysis of the A2A protocol. We introduce a novel framework that utilizes an LLM-assisted extraction of a verified finite-state machine directly from the natural-language specification, producing a unified model of 37 states and 76 transitions from 929 formalized statements, and then systematically reasons over this model to discover protocol-level vulnerabilities through adversarial verification, under a full-compliance assumption. Our analysis uncovers 11 new vulnerabilities, each exploitable by a specification-compliant adversary without requiring any implementation flaw. Among the findings are cross-client context injection through unprotected context identifiers, credential harvesting via multi-hop identity loss in delegation chains, and data exfiltration through rogue agents advertising unattested capability claims. A2ABreak achieves 73.3% precision and 84.6% F1 against manual expert review, while a zero-shot LLM baseline operating over the same specification produces zero confirmed findings, demonstrating that explicit formal grounding is essential for sound protocol security analysis. ## Agentic partners integration: AI support agents for Microsoft Azure Red Hat OpenShift DevFeed: [Agentic partners integration: AI support agents for Microsoft Azure Red Hat OpenShift]() Original publisher: [Read original article]() Author: Sharon Dashet, Carlos Camacho Gonzalez Published: 2026-10-22T13:01:36Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Azure](), [AI Integration Strategies](), [redhat](), [AI for customer service automation](), [AI Chat]() Tags: [a2a-protocol](), [access-control](), [agentic](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-observability](), [ai-platform](), [aks](), [alert](), [alerts](), [artificial-intelligence](), [azure](), [demo](), [deployment](), [developer](), [microsoft](), [openshift](), [support](), [use-cases]() ### AI overview The Azure Red Hat OpenShift partner agent investigates live Azure infrastructure by discovering tools from a containerized Microsoft Azure MCP server, invoking them through an LLM tool-calling loop, and combining results across services. In the example, it checks cluster health, memory metrics, and resource-group recommendations to diagnose OOMKilled pods. The server provides Azure management operations; the agent reasons over their results. Access is gated by Keycloak identity and OPA department policies, Azure access defaults to the read-only Reader role, and calls are logged for audit. The excerpt also gives OpenShift deployment routes: a Helm chart requiring OpenShift 4.12+, Helm 3.8+, and an LLM API key, or the OpenShift AI MCP catalog, which is described as a Technology Preview requiring three enablement steps. ### Source excerpt Red Hat AI quickstarts are ready-to-run demo applications that demonstrate real-world AI use cases, designed for seamless deployment on the Red Hat AI platform. The post Agentic partners integration: AI support agents for Microsoft Azure Red Hat OpenShift appeared first on Red Hat Developer. ## This Week in Open Source for October 15, 2026 DevFeed: [This Week in Open Source for October 15, 2026]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-10-15T18:30:00Z Content type: article Language: en Sources: [Google Open Source Blog]() Topics: [Open Source](), [Developer Events](), [A2A protocol](), [a2ui](), [Open Source Models & Datasets]() Tags: [2026](), [a2a-protocol](), [agent](), [agent2agent](), [agentic](), [agentic-ai](), [ai](), [ai-engineering](), [california](), [community](), [conference](), [conferences](), [ecosystem](), [embedded](), [events](), [flutter](), [gemma](), [generative-ui](), [news](), [october](), [open-source](), [this-week-in-open-source](), [twios]() ### AI overview The roundup centers on human control and trust as open protocols and AI agents enter everyday workflows. Its listed reads include an Apache 2.0 A2A CLI for discovering and messaging agents and wrapping stdin/stdout scripts as streaming servers; a2term, which presents interactive terminal confirmations before agents act; and an article questioning how trust carries over when agents mediate headless software workflows. It also highlights EmbeddingGemma 2, described as an Apache 2.0 multimodal embedding model that fits search into 567 MB of RAM for on-device use, and sandboxed AT Protocol Web Tile embeds. A Linux Foundation/TODO Group survey reports that 79% of AI-involved OSPOs shape model policy and 69% pilot agentic workflows. The excerpt frames inspectable tools, clear boundaries, human agency, and maintainer sustainability as key concerns. ### Source excerpt by Daryl Ducharme, Open Source Programs Office This Week in Open Source for October 15, 2026 A look around the world of open source After speaking about the Agent2Agent (A2A) protocol and Model Context Protocol (MCP) in Toronto last week (which I recapped in Tag-Team Transmission), open agent boundaries were clearly still on my mind while collecting this week's reads. As AI agents, terminal tools, and embedded micro-apps move deeper into everyday software workflows, a central tension keeps surfacing across the open source community: where do human judgment, interface trust, and maintainer sustainability fit in? Whether teams are wiring up open protocols in the Unix shell, running multimodal retrieval locally on a laptop, or stewarding decades-old kernel infrastructure, the healthiest open source architectures don't replace human agency--they give workers clearer boundaries, inspectable tools, and a genuine seat at the controls. Upcoming Events 🗓 October 2026 All Things Open 2026 (October 19-20, 2026) -- Raleigh, North Carolina, USA. One of the largest community-focused open source conferences on the U.S. East Coast exploring open source software, AI engineering, and maintainer sustainability--featuring Googlers Andrew Brogdon, Mark Techson, and Marc Dougherty speaking on "Where Is My Futuristic App? Generative UI and the Open Future" and Abdel Sghiouar on "Taming Agentic AI: How to Run Agents-Generated Code Safely on Kubernetes", alongside the Google Community Lounge with the Flutter team. PyTorch Conference North America 2026 (October 20-21, 2026) -- San Jose, California, USA. Two days of open source machine learning innovation across the PyTorch ecosystem--featuring a keynote by Google's Bill Jia ("Workload Fungibility in the Age of Agents") and technical sessions from Googlers Chris Jones, Sandeep Pokkunuri, Rob Mulla, Ankita Luthra, Trinadh Kotturu, Claudio Basile, Qi Zhou, Jago Macleod, and Aleksey Vlasenko on TorchTPU, vLLM, SGLang, Ray, and Kubernetes. AGNTCon + ## A2A agent cards DevFeed: [A2A agent cards]() Original publisher: [Read original article]() Author: Joost de Valk Published: 2026-10-15T00:00:00Z Content type: tutorial Language: en Sources: [The Website Specification]() Topics: [A2A protocol](), [agent to agent communication](), [Remote Procedure Call (RPC)](), [gRPC]() Tags: [a2a-protocol](), [agent-readiness](), [discovery](), [json](), [json-rpc](), [optional](), [rpc]() ### AI overview A2A agent cards are JSON manifests served at `/.well-known/agent-card.json` that let agents discover another agent's skills, capabilities, and endpoints. The article explains the required and optional card fields, transport choices, publishing and compatibility guidance, common mistakes, and validation steps for implementing a card aligned with A2A 1.0. ### Source excerpt The Agent-to-Agent (A2A) protocol lets an autonomous agent find another autonomous agent and call it over JSON-RPC. Discovery hinges on a single well-known file: `/.well-known/agent-card.json`. Relevant whenever your service exposes agentic behaviour another agent might want to delegate to. ## Rossoctl for agent discoverability, security, and observability on Kubernetes DevFeed: [Rossoctl for agent discoverability, security, and observability on Kubernetes]() Original publisher: [Read original article]() Author: Shri Charan Published: 2026-10-06T13:01:21Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [agent observability](), [agent to agent communication](), [observability ai agents](), [Plover]() Tags: [a2a-protocol](), [agentic](), [agentruntime](), [agents](), [artificial-intelligence](), [developer](), [kubernetes](), [observability](), [security]() ### AI overview The article presents Rossoctl as a Kubernetes controller for discovering and managing AI agents across a cluster. It describes A2A AgentCards for publishing agent capabilities, a layered security approach using short-lived certificates, mTLS, signed cards, and scoped tokens, and MLflow tracing for agent decisions and tool calls. It notes that this architecture is most appropriate for multiple teams, cross-agent dependencies, or demanding compliance and debugging needs. ### Source excerpt Imagine deploying a pair of agents to Red Hat OpenShift and watching them thrive. But fast-forward 6 months and you find your environment cluttered with 40 of them, their purposes largely unknown. Redundant agents emerge across namespaces, duplicating work. Meanwhile, a security review uncovers agents utilizing static API keys over insecure HTTP. When an agent falters, the lack of tracing makes it impossible to distinguish between model hallucinations, tool errors, or downstream failures. The post Rossoctl for agent discoverability, security, and observability on Kubernetes appeared first on Red Hat Developer. ## This Week in Open Source for October 2, 2026 DevFeed: [This Week in Open Source for October 2, 2026]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-10-02T18:30:00Z Content type: article Language: en Sources: [Google Open Source Blog]() Topics: [Developer Events](), [AI Infrastructure](), [Open Source Models & Datasets](), [A2A protocol](), [valkey](), [linux foundation](), [Flutter]() Tags: [2026](), [a2a-protocol](), [agent](), [agent2agent](), [agentic](), [agentic-ai](), [ai](), [apache-software-foundation](), [benchmarks](), [community](), [community-over-code](), [conference](), [control](), [data-structures](), [developer](), [developers](), [development](), [documentation](), [events](), [gemma](), [news](), [october](), [open-source](), [this-week-in-open-source](), [twios]() ### AI overview The roundup argues that open-source AI's next phase depends on infrastructure, transparent local tooling, and community norms that keep developers and maintainers in control. Its selected reads illustrate this focus: a Mozilla-Mila initiative with Canadian government support; EnvHarness, which adapts agent training sandboxes to failure modes; a calculation-by-calculation trace of Gemma 4 12B inference; an offline RAG setup pairing Qdrant Edge and Google LiteRT; and UISurf's cross-environment UI-agent orchestration using A2A, with attention to sandboxing and human oversight. It also highlights concerns that unvetted LLM code and bounty incentives can strain maintainers and community trust. The excerpt includes event listings and editorial questions, but gives limited detail about the linked projects and articles. ### Source excerpt by Daryl Ducharme, Open Source Programs Office This Week in Open Source for October 2, 2026 A look around the world of open source As AI becomes part of everyday software development, the most compelling questions in open source are shifting from the models themselves to everything surrounding them. I'm always drawn to how technology affects individuals, and especially the workers who use and maintain it, for better or for worse (I definitely prefer better). That's why I'm convinced the next phase of open-source AI won't be won by chasing bigger benchmarks, but by building the open infrastructure, transparent local tooling, and community norms that keep developers and maintainers in control. This week's reads explore what that looks like in practice. Upcoming Events 🗓 October 2026 ValkeyConf 2026 (October 5, 2026) -- Prague, Czechia. Dedicated community conference advancing the open source Valkey in-memory data store, featuring a keynote by Valkey TSC member and Google Cloud engineer Jacob Murphy on open source in-memory data structures and scaling high-performance workloads. MCP Dev Summit Toronto (October 5-6, 2026) -- Toronto, Ontario, Canada. Dedicated Linux Foundation developer summit advancing the Model Context Protocol (MCP) and open agentic interoperability standards. Join Google OSPO's Daryl Ducharme (that's me!) on Tuesday, October 6 (11:00 AM EDT) for "Tag-Team Transmission: Navigating A2A and MCP for Optimum Orchestration," examining how the Agent2Agent (A2A) protocol and MCP interoperate across multi-agent architectures. Open Source Summit Europe (October 7-9, 2026) -- Prague, Czechia. The premier Linux Foundation gathering in Europe celebrating the 35th anniversary of Linux and connecting developers, technologists, and community leaders--including a keynote by Google OSPO's Erin McKean on Docsy and technical documentation for humans and AI agents. Community Over Code (October 11-14, 2026) -- Glasgow, Scotland. The flagship Apache Software Foundation confer ## Introducing CLARA Agent: Instant Cloud & AI Risk Insights Available on Google Cloud Gemini Enterprise DevFeed: [Introducing CLARA Agent: Instant Cloud & AI Risk Insights Available on Google Cloud Gemini Enterprise]() Original publisher: [Read original article]() Author: Geetha Anandakrishnan Published: 2026-09-29T20:15:30Z Content type: release Language: en Sources: [Palo Alto Networks]() Topics: [AI software supply chain security](), [Google Cloud Platform (GCP)](), [Local AI]() Tags: [a2a-protocol](), [announcement](), [clara](), [cloud-security](), [google-cloud](), [partners](), [products-and-services](), [security]() ### AI overview Palo Alto Networks is introducing CLARA Agent, which connects Strata Cloud Manager to Google Cloud Gemini Enterprise so teams can ask plain-language questions about cloud security data. The product is described as surfacing AI and shadow AI workloads, discovering infrastructure, and prioritizing vulnerabilities. The post also says the integration uses A2A and MCP and will be available through Google Cloud Marketplace. ### Source excerpt Most cloud and security teams struggle with two fundamental questions: What is actually running across our clouds, and what is exposed? As workloads sprawl and AI models deploy faster than ever, critical ... The post Introducing CLARA Agent: Instant Cloud & AI Risk Insights Available on Google Cloud Gemini Enterprise appeared first on Palo Alto Networks Blog. ## This Week in Open Source for September 24, 2026 DevFeed: [This Week in Open Source for September 24, 2026]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-09-24T18:30:00Z Content type: article Language: en Sources: [Google Open Source Blog]() Topics: [Open Source](), [lunar foundation model](), [A2A protocol](), [Model Context Protocol](), [openssf](), [Digital Public Good]() Tags: [2](), [2026](), [a2a](), [a2a-protocol](), [adapting](), [agent](), [agent2agent](), [agentic](), [ai](), [ai-engineering](), [ai-security](), [all](), [anniversary](), [apache](), [apache-software-foundation](), [events](), [governance](), [news](), [open](), [open-source](), [source](), [sustainability](), [this](), [this-week-in-open-source](), [twios]() ### AI overview Open source's economic value is substantial, but sustaining its maintainers and governance calls for practical funding and evidence of benefits. The excerpt highlights a proposal for commercial package registries and mirror vendors to pay automatic royalties down dependency trees, and an LF Energy report finding 2-5x greater net value for regulated grid operators under an auditable benefit-cost framework. It also argues that examining autonomous AI security failures can inform stronger supply-chain defenses. In open science, IBM and NASA released a Lunar Foundation Model alongside a harmonized, machine-learning-ready lunar dataset combining more than 30 instrument layers. The newsletter also lists October and November open source events. These points reflect the supplied excerpt; it does not provide the full linked articles or reports. ### Source excerpt by Daryl Ducharme, Open Source Programs Office This Week in Open Source for September 24, 2026 A look around the world of open source Open source underpins an $8.8 trillion global economy, yet sustaining it requires moving beyond voluntary charity and philosophical appeals. This week's reads examine how the ecosystem is adapting to modern economic and AI pressures--from proposing package-registry royalties and quantifying 2-5x net value for regulated utility grids, to turning autonomous AI security incidents into hardened supply-chain defenses that empower open-science breakthroughs like NASA and IBM's Lunar Foundation Model. Upcoming Events 🗓 October 2026 MCP Dev Summit Toronto (October 5-6, 2026) -- Toronto, Ontario, Canada. Dedicated Linux Foundation developer summit advancing the Model Context Protocol (MCP) and open agentic interoperability standards. Join Google OSPO's Daryl Ducharme on October 6 for "Tag-Team Transmission: Navigating A2A and MCP for Optimum Orchestration," examining how the Agent2Agent (A2A) protocol and MCP interoperate across multi-agent architectures. Open Source Summit Europe (October 7-9, 2026) -- Prague, Czechia. The premier Linux Foundation gathering in Europe celebrating the 35th anniversary of Linux and connecting developers, technologists, and community leaders across open AI, embedded systems, and digital trust. Community Over Code (October 11-14, 2026) -- Glasgow, Scotland. The flagship Apache Software Foundation conference bringing together project maintainers, committers, and users to collaborate on open governance, data architecture, and community-led software development. All Things Open 2026 (October 19-20, 2026) -- Raleigh, North Carolina, USA. One of the largest community-focused open source conferences on the U.S. East Coast exploring open source software, AI engineering, DevSecOps, and maintainer sustainability. GitHub Universe 2026 (October 28-29, 2026) -- San Francisco, California, USA & Virtual. Annual global developer gath ## From Isolated Agents to Collective Intelligence: Why A2A Is the Protocol the Agentic SOC Has Been Waiting For DevFeed: [From Isolated Agents to Collective Intelligence: Why A2A Is the Protocol the Agentic SOC Has Been Waiting For]() Original publisher: [Read original article]() Author: Jeff Yeo Published: 2026-08-28T15:00:11Z Content type: article Language: en Sources: [Security @ Cisco]() Topics: [A2A protocol](), [Agentic SOC](), [agent to agent communication](), [Model Context Protocol](), [Multi Agent Systems](), [software-architecture](), [Security Operations Center](), [Security]() Tags: [a2a](), [a2a-protocol](), [agentic](), [agentic-ai](), [agentic-soc](), [agents](), [artificial-intelligence-ai](), [mcp](), [multi-agent](), [multi-agent-security](), [protocol](), [security](), [security-operations](), [soc]() ### AI overview A2A is presented as a way for agents across platforms to delegate security work directly, addressing handoffs that otherwise require a human to move results between tools. It complements MCP, which connects agents to data and tools such as Splunk: MCP is the agent-to-data layer, while A2A is the agent-to-agent layer. Unlike centrally managed SOAR playbooks, A2A is described as peer-to-peer, with agents advertising capabilities in Agent Cards and delegating tasks that return artifacts or status updates. It uses HTTP, SSE and JSON-RPC 2.0, with authentication through familiar schemes such as OAuth 2.0, API keys and mTLS; signed cards in A2A v1.0 can verify issuer identity. The excerpt says production security-agent handoffs are not shipping yet. It recommends preparing agent identity governance, durable delegation logs and input validation: A2A does not sanitize attacker-controlled task content. Splunk can ingest traffic logs, but using them for Enterprise Security searches and risk scoring requires CIM mapping or a custom data model. ### Source excerpt The Agentic SOC needs two protocols, not one. Learn how MCP and A2A work together as the vertical and horizontal layers of multi-agent security operations ## LangChain Announces Enterprise Agentic AI Platform Built with NVIDIA DevFeed: [LangChain Announces Enterprise Agentic AI Platform Built with NVIDIA]() Original publisher: [Read original article]() Author: LangChain Accounts Published: 2026-08-26T16:15:01Z Content type: release Language: en Sources: [LangChain Blog]() Topics: [Agentic AI](), [LangChain](), [Nemotron](), [NVIDIA DGX](), [NVIDIA NIM](), [Quasar]() Tags: [a2a-protocol](), [agentic](), [agentic-ai](), [agentic-ai-development](), [agents](), [ai](), [ai-agents](), [ai-platform](), [deploy](), [langchain](), [monitor](), [nvidia](), [production](), [scale]() ### AI overview LangChain announced an enterprise agentic AI platform integrated with NVIDIA tools and models. It combines LangChain frameworks and LangSmith with NVIDIA technologies for building, deploying, evaluating, and monitoring AI agents. The release describes workflow profiling and optimization, security guardrails, observability, and model evaluation, and says the integration is available today. ### Source excerpt Build, deploy, and monitor production-grade AI agents at scale with LangChain's enterprise agentic AI platform integrated with NVIDIA. ## Why Yugabyte Joined the Agentic AI Foundation DevFeed: [Why Yugabyte Joined the Agentic AI Foundation]() Original publisher: [Read original article]() Author: Heather Downing Published: 2026-08-20T14:09:05Z Content type: article Language: en Sources: [The Distributed SQL Blog]() Topics: [Agentics Foundation](), [engineering-leadership](), [Open Source]() Tags: [a2a-protocol](), [agentic-ai](), [ai](), [ai-agents](), [data-infrastructure](), [database](), [databases](), [meko](), [yugabyte](), [yugabytedb](), [yugabytedb-amp]() ### AI overview Yugabyte says it joined the Linux Foundation's Agentic AI Foundation as a Silver Member to help shape standards for agent infrastructure. The post describes the foundation's projects, including MCP, goose, AGENTS.md, and A2A, and presents Yugabyte's agent-focused database and context products as part of its rationale. ### Source excerpt After a decade of building a reliable distributed data architecture, the natural progression for Yugabyte was to begin supporting AI agents. Yugabyte is now a Silver Member of the Agentic AI Foundation (AAIF), the Linux Foundation's home for the open standards that agentic AI runs on. This blog provides background on the AAIF's work and shares why we joined the Foundation. The post Why Yugabyte Joined the Agentic AI Foundation appeared first on Yugabyte. ## A2A Java SDK 1.2.0.Final released DevFeed: [A2A Java SDK 1.2.0.Final released]() Original publisher: [Read original article]() Author: Kabir Khan (https://twitter.com/kabirkhan) Published: 2026-08-18T00:00:00Z Content type: release Language: en Sources: [Quarkus - Supersonic Subatomic Java]() Topics: [A2A protocol](), [SDK](), [Authorization](), [Java](), [Quarkus](), [Remote Procedure Call (RPC)](), [Security](), [migration]() Tags: [a2a](), [a2a-protocol](), [authorization](), [grpc](), [java](), [migration](), [quarkus](), [release](), [rpc](), [sdk]() ### AI overview The A2A Java SDK 1.2.0.Final release hardens authorization, adds task stream lifecycle management, fixes protocol compliance issues, and changes referenced-task population behavior. It also includes breaking changes and migration requirements. ### Source excerpt I am happy to announce the release of A2A Java SDK 1.2.0.Final. This release hardens the authorization model, adds stream lifecycle management, and fixes several protocol compliance issues. The A2A Java SDK is the official Java implementation of the A2A Protocol, with Quarkus as its reference runtime. The SDK's CDI-based programming model -- used throughout the examples below -- integrates naturally with Quarkus, and the examples in the repository are all Quarkus applications. This release contains breaking changes. See the Migration from 1.1.0.Final section for details. What's New Authorization Hardening The 1.1.0 release introduced the TaskAuthorizationProvider SPI for per-user task authorization. This release closes gaps in that model and makes the authorization API easier to use from non-CDI environments. Read authorization on referenced tasks Previously, when a request referenced existing tasks via referenceTaskIds, the SDK populated those tasks in the RequestContext without checking read authorization. An unauthorized caller could probe for task existence via sendStreamingMessage or subscribeToTask. This release enforces read authorization on all referenced task lookups (#1005). The fix touches all transports (JSON-RPC, gRPC, REST) and decorators (OpenTelemetry, authorization). InMemoryTaskStore now applies fail-closed authorization -- when authorization is configured but no call context is available, access is denied -- matching the existing JpaDatabaseTaskStore behavior. This is a breaking change -- see 1. Read authorization on referenced task lookup below. Referenced task population now enabled by default Previously, when using DefaultRequestHandler via CDI, referenced task IDs in messages were not resolved from the TaskStore. This has been fixed -- referenced tasks are now populated by default and made available via RequestContext.getRelatedTasks(). If you need to restore the previous behavior, set the following property: a2a.request-context.populate-referred-ta ## 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: news Language: en Sources: [Google Open Source Blog]() Topics: [Open Source](), [open source communities](), [google-adk](), [open source maintenance](), [A2A protocol](), [linux foundation](), [gRPC]() Tags: [2025](), [a2a-protocol](), [agent2agent](), [agents](), [ai](), [applications](), [enterprise](), [framework](), [github](), [google-open-source](), [linux](), [linux-foundation](), [open-source](), [open-source-communities](), [open-source-projects](), [partners](), [report-card](), [retrospective](), [security]() ### AI overview Alphabet reports that roughly 10% of its full-time workforce contributed to open source in 2025, a share steady over five years; its contributors worked on community-led projects such as LLVM and Rust as well as Google-initiated projects, and its projects received commits from over 20,000 non-Alphabet accounts. Google worked with more than 50 partners on the Agent2Agent protocol before donating it to the Linux Foundation. Its highlighted releases included lightweight Gemma 3 open models and the Agent Development Kit for building AI agents. Security efforts included CodeMender, which upstreamed 72 fixes in six months; OSS Rebuild, which checks package build provenance; OSV-Scalibr for vulnerability scanning; and credential scanning on deps.dev. The OSPO provided $2 million to over 40 projects, while Google Summer of Code supported 1,280 contributors across 185 organizations. The report says it is based on activity from public GitHub and internal Git-on-Borg repositories, a subset of tracked activity; account counts do not necessarily represent unique people. ### 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: [KodeKloud]() 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. ## Passing Context Between Agents in Multi-Agent A2A Systems DevFeed: [Passing Context Between Agents in Multi-Agent A2A Systems]() Original publisher: [Read original article]() Author: Olha Konstantinova, Scott Grosenick Published: 2026-06-26T07:00:00Z Content type: article Language: en Sources: [ISE Developer Blog]() Topics: [A2A protocol](), [Multi Agent Systems](), [agent to agent communication](), [stateless](), [Artificial Intelligence](), [Microservices](), [systems]() Tags: [a2a](), [a2a-protocol](), [agent](), [agent-communication](), [agent-to-agent-communication](), [agents](), [architecture](), [backend](), [context-passing](), [cse](), [ise](), [multi-agent](), [stateless]() ### AI overview This article explains how to pass conversational and domain-specific context between independently deployed agents in a multi-agent system using the A2A protocol. It describes the context-passing challenge created by stateless domain agents and introduces an embedded context pattern. ### Source excerpt How we solved the challenge of passing conversational context between independently deployed agents using the A2A protocol's embedded context pattern, keeping domain agents stateless and secure. The post Passing Context Between Agents in Multi-Agent A2A Systems appeared first on ISE Developer Blog. ## A2A in Java: hands on Embabel DevFeed: [A2A in Java: hands on Embabel]() Original publisher: [Read original article]() Author: Aneshka Goyal Published: 2026-06-21T07:01:03Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Embabel](), [A2A protocol](), [Model Context Protocol](), [Maven](), [Spring Framework](), [Amazon Bedrock]() Tags: [a2a](), [a2a-protocol](), [agent](), [ai](), [bedrock](), [embabel](), [java](), [llm](), [local](), [maven](), [mcp](), [mcp-server](), [review](), [software-engineering](), [spring-ai](), [spring-boot](), [story]() ### AI overview The example builds a Java story-writing agent with Embabel that uses Bedrock for LLM calls, a local Spring AI MCP server to supply a story word count, and A2A HTTP endpoints to accept requests. A project creator generates the starter skeleton; Maven includes Embabel's Bedrock and A2A starters. The agent exposes a goal that writes and reviews a story, while its action arguments provide prerequisites and a configured tool group makes MCP tools available. The article notes that servlet mode must be enabled for A2A endpoints, and the generator's OpenAI and Anthropic integrations are unused. In the demonstrated request, an A2A message asking for a rabbit story triggers the MCP tool and returns a completed task containing the story and review. ### Source excerpt Part 2: Hands on with Embabel! In Part 1, we discussed how Embabel works internally. In Part 2, we'll now try to get an application started that has an LLM interaction, talks to local MCP server (for tools), and helps us achieve a goal. This time, we'll also make sure we use A2A protocol for our agent (something Embabel makes easier to do allowing us to focus on core business logic and offloading boilerplate code to Embabel). We'll use the project creator to create a skeleton project for us with some Embabel dependencies and code. uvx --from git+https://github.com/embabel/project-creator.git project-creator Please note we can use Java or Kotlin as our preferred language, I would be leveraging Java. This is a simple story teller agent who writes a story and gets it reviewed (both story generation and review comes from LLM). We'd be using Bedrock for connection to our LLM model. For fetching the word count for a topic, it uses an MCP server which is a Spring AI MCP server running locally. Dependency management is handled using Maven, and the POM file looks like this: 4.0.0 org.springframework.boot spring-boot-starter-parent 3.5.9 com.example.demo-city-agent Demo-city-agent 0.1.0-SNAPSHOT jar My first agent Generated agent project 21 0.3.1 com.embabel.agent ) Original publisher: [Read original article]() Author: Aneshka Goyal Published: 2026-06-16T08:44:44Z Content type: article Language: en Sources: [Amex GBT Technology]() Topics: [Embabel](), [A2A protocol](), [Spring Framework](), [Microsoft Agent Framework](), [Artificial Intelligence](), [AI Chat](), [Python]() Tags: [a2a](), [a2a-protocol](), [actions](), [adk](), [agent](), [agent-framework](), [agentic](), [agents](), [ai](), [ai-agents](), [ai-applications](), [applications](), [artificial-intelligence](), [building](), [compliance](), [domain](), [embabel](), [framework](), [frameworks](), [java](), [software-engineering](), [spring-ai]() ### AI overview The article explains how Embabel, a Java agent framework built on Spring AI, supports A2A communication and uses planning to connect LLMs with existing business logic. Its A2A endpoints expose agent cards and JSON-RPC calls; a request handler passes messages to Autonomy, which ranks available agents from user input and metadata, then creates an agent process. Embabel's GOAP planner targets a defined goal and can replan after each action, while its utility planner selects the highest-value next action for reactive or exploratory tasks and may stop when no action qualifies. The agent process maintains execution state on a type-indexed blackboard. Spring AI can also be used without Embabel to build agents. ### 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 ## A2A Java SDK 1.0.0.Final Released DevFeed: [A2A Java SDK 1.0.0.Final Released]() Original publisher: [Read original article]() Author: Kabir Khan (https://twitter.com/kabirkhan) Published: 2026-06-10T00:00:00Z Content type: release Language: en Sources: [Quarkus - Supersonic Subatomic Java]() Topics: [A2A protocol](), [Artificial Intelligence](), [Java](), [releases](), [SDK](), [Remote Procedure Call (RPC)](), [Quarkus](), [linux foundation]() Tags: [a2a](), [a2a-protocol](), [ai](), [java](), [linux-foundation](), [quarkus](), [releases](), [rpc](), [sdk]() ### AI overview Quarkus announces the first GA release of the A2A Java SDK, an official Java implementation of the Agent2Agent Protocol. The release supports AI-agent interoperability across languages and frameworks, with JSON-RPC, gRPC, and HTTP+JSON/REST transports, plus an Integration Test Kit for protocol-compliance validation. ### Source excerpt I am pleased to announce the release of A2A Java SDK 1.0.0.Final -- our first GA release. The A2A Java SDK is the official Java implementation of the Agent2Agent (A2A) Protocol, an open standard that enables AI agents to communicate and collaborate regardless of underlying framework, language, or vendor. This release is the result of six months and seven pre-releases (four Alphas, a Beta, and a Candidate Release), with contributions from 17 people. If you've been tracking the pre-releases, you can upgrade from CR1 with no breaking changes. What's A2A? The Agent2Agent (A2A) Protocol is an open standard, governed by the Linux Foundation, that lets AI agents discover each other's capabilities, delegate tasks, and collaborate -- even if they're written in different languages or built on different frameworks. For example, an orchestrator agent written in Python can delegate to a specialist agent written in Java. The A2A Java SDK provides everything you need to build A2A server agents and clients in Java, with reference implementations based on Quarkus and community integration with WildFly/Jakarta EE. Installation Import the BOM and add the dependencies for your chosen transport: org.a2aproject.sdk a2a-java-sdk-bom 1.0.0.Final pom import org.a2aproject.sdk a2a-java-sdk-reference-jsonrpc org.a2aproject.sdk a2a-java-sdk-reference-grpc org.a2aproject.sdk a2a-java-sdk-reference-rest All three transports -- JSON-RPC, gRPC, and HTTP+JSON/REST -- are fully supported and considered equal. Just pick the artifact(s) for the transport(s) you need. What' ## A2A Java SDK 1.0.0.CR1 Released DevFeed: [A2A Java SDK 1.0.0.CR1 Released]() Original publisher: [Read original article]() Author: Kabir Khan (https://twitter.com/kabirkhan) Published: 2026-05-18T00:00:00Z Content type: release Language: en Sources: [Quarkus - Supersonic Subatomic Java]() Topics: [A2A protocol](), [Java](), [SDK](), [releases](), [Java HttpClient](), [HTTP](), [Quarkus](), [gRPC]() Tags: [a2a](), [a2a-protocol](), [api](), [docker](), [http](), [java](), [maven](), [opentelemetry](), [podman](), [push-notification](), [quarkus](), [release](), [releases](), [sdk](), [sse](), [standard](), [version]() ### AI overview This release announces A2A Java SDK 1.0.0.CR1, a candidate release for the Agent2Agent protocol. It adds compatibility between protocol versions 0.3 and 1.0, an Android HTTP client, unified HTTP client implementations, and a spec-compliant SSE parser. The SDK provides Java implementations based on Quarkus and supports JSON-RPC, gRPC, and REST transports. ### Source excerpt I am pleased to announce the release of A2A Java SDK 1.0.0.CR1. CR (Candidate Release) means all features planned for 1.0 are now complete -- this is the last step before GA. There are no breaking changes from Beta1, so upgrading is straightforward. The big addition in this release is a v0.3 protocol version compatibility layer, so your v1.0 agents can interoperate with agents and clients still running v0.3. We also added an Android HTTP client, unified HTTP client implementations, and a spec-compliant SSE parser. What's A2A? The Agent2Agent (A2A) Protocol is an open standard that enables AI agents to communicate and collaborate with one another, regardless of each agent's underlying framework, language, or vendor. The A2A Java SDK makes it easy to build A2A-compliant agents in Java, with reference implementations based on Quarkus. The Journey to 1.0 Since January 2026, we have gone through six pre-releases to get here. Here is a brief recap: 1.0.0.Alpha1 (January 2026) -- Aligned with the draft A2A 1.0 specification: migrated to Java records, switched from Jackson to Gson, introduced BOMs, and added comprehensive Javadoc. 1.0.0.Alpha2 (February 2026) -- Added OpenTelemetry-based telemetry, push notification support, and the streamlined AgentEmitter API. 1.0.0.Alpha3 / Alpha4 (February-March 2026) -- Maven coordinates changed to org.a2aproject.sdk, various protocol alignment improvements, and the addition of Docker/Podman test utilities. 1.0.0.Beta1 (April 2026) -- Fully aligned with the final A2A Specification 1.0.0, renamed Java packages to org.a2aproject.sdk.*, added structured error codes, and achieved equal support across all three transports. Each of those blog posts covers its respective release in detail. Installation To use A2A Java SDK 1.0.0.CR1, import the BOM and add the dependencies you need: org.a2aproject.sdk a2a-java-sdk-bom 1.0.0.CR1 po ## A2A Java SDK 1.0.0.Beta1 Released DevFeed: [A2A Java SDK 1.0.0.Beta1 Released]() Original publisher: [Read original article]() Author: Emmanuel Hugonnet (https://twitter.com/ehsavoie) Published: 2026-04-27T00:00:00Z Content type: release Language: en Sources: [Quarkus - Supersonic Subatomic Java]() Topics: [A2A protocol](), [SDK](), [Java](), [Maven](), [standard](), [gRPC](), [JSON](), [Maven Central](), [Cache](), [Error Handling](), [Quarkus]() Tags: [a2a](), [a2a-protocol](), [agent2agent](), [cache](), [error-handling](), [github](), [grpc](), [java](), [json](), [json-rpc](), [maven](), [maven-central](), [quarkus](), [sdk]() ### AI overview Quarkus announces A2A Java SDK 1.0.0.Beta1, aligned with the stable A2A Specification 1.0.0. The release introduces package and Maven coordinate changes, equal support for JSON-RPC, gRPC, and HTTP+JSON/REST transports, structured error details, HTTP caching headers, and more flexible Java HTTP client construction. ### Source excerpt We are pleased to announce the release of A2A Java SDK 1.0.0.Beta1, our first release fully aligned with the final A2A Specification 1.0.0. The A2A Protocol has reached stability as an open standard, and this release marks the Java SDK tracking that maturity: protocol conformance is the central focus, and the SDK coordinates now reflect the project's identity under the A2A Project organization. As a sign of this maturity, two structural breaking changes accompany this release: Java packages have been renamed from io.a2a. to org.a2aproject.sdk. The Maven groupId has changed from io.github.a2asdk to org.a2aproject.sdk (first introduced in 1.0.0.Alpha4) What's A2A? The Agent2Agent (A2A) Protocol is an open standard that enables AI agents to communicate and collaborate with one another, regardless of each agent's underlying framework, language, or vendor. The A2A Java SDK makes it easy to build A2A-compliant agents in Java, with reference implementations based on Quarkus. Breaking Changes New Maven Coordinates The Maven groupId has changed from io.github.a2asdk to org.a2aproject.sdk. If you are upgrading from any previous release, you must update your build files accordingly. The root artifact coordinates were renamed to reflect the project's new home under the A2A Project organization and its availability on Maven Central. Update your pom.xml dependencies accordingly: org.a2aproject.sdk a2a-java-sdk-bom 1.0.0.Beta1 pom import org.a2aproject.sdk a2a-java-sdk-client org.a2aproject.sdk a2a-java-sdk-reference-jsonrpc org.a2aproject.sdk ## 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 [Next page]()