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Alice Labs uses A2A whenever a LangGraph, Microsoft Agent Framework, or Google ADK agent needs to call another agent across a service or organizational boundary. It reached v1.0 in April 2026 and is governed by the Linux Foundation." } }, { "@type": "Question", "name": "Who created the A2A protocol?", "acceptedAnswer": { "@type": "Answer", "text": "Google Cloud introduced A2A in April 2025 with 50+ launch partners including Salesforce, SAP, Accenture, and Deloitte. In June 2025, Google donated the protocol, specification, and SDKs to the Linux Foundation, which established the Agent2Agent project for vendor-neutral governance. Alice Labs treats the Linux Foundation spec at a2a-protocol.org as the canonical source of truth for client implementations." } }, { "@type": "Question", "name": "What is the current A2A spec version?", "acceptedAnswer": { "@type": "Answer", "text": "A2A v1.0.0 is the current stable specification as of April 2026. It introduced signed Agent Cards, an extension mechanism, and multi-transport support (JSON-RPC 2.0, gRPC, and HTTP+JSON/REST). Previous releases (0.1.0, 0.2.6, 0.3.0) remain documented for migration purposes. Alice Labs recommends targeting v1.0 for all new production integrations and migrating older 0.x implementations by end of 2026." } }, { "@type": "Question", "name": "How does A2A compare to MCP?", "acceptedAnswer": { "@type": "Answer", "text": "MCP (Model Context Protocol) standardizes how one agent talks to tools, data, and APIs. A2A standardizes how multiple agents talk to each other. They solve different layers and most Alice Labs production stacks use both: MCP inside each agent for tool access, A2A between agents for coordination. Reference pattern: an inventory agent uses MCP to check stock, then A2A to delegate a purchase order to a supplier's remote agent." } }, { "@type": "Question", "name": "Is A2A production-ready in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. A2A hit v1.0 stable in April 2026 with 150+ supporting organizations, 22,000+ GitHub stars, and production-ready SDKs in Python, JavaScript, Java, Go, and .NET. GA support ships inside Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock AgentCore, and Google Vertex AI. Alice Labs has shipped A2A meshes into regulated Nordic and European enterprises using signed Agent Cards and mTLS." } }, { "@type": "Question", "name": "What are Agent Cards in A2A?", "acceptedAnswer": { "@type": "Answer", "text": "An Agent Card is a JSON document an agent publishes at a well-known path (typically /.well-known/agent-card.json). It declares identity, endpoint URL, skills, input/output modalities, and supported authentication schemes. Clients fetch the card, verify it, and know exactly how to call the agent. v1.0 added Signed Agent Cards using JSON Web Signature so callers can cryptographically verify the card was issued by the claimed agent." } }, { "@type": "Question", "name": "What transport protocols does A2A use?", "acceptedAnswer": { "@type": "Answer", "text": "A2A v1.0 defines three transport bindings: JSON-RPC 2.0 over HTTP or WebSocket (reference), gRPC (for typed streaming), and HTTP+JSON/REST. All three carry the same Task, Message, and Artifact model. Server-Sent Events handles streaming Task updates; webhooks handle push notifications for long-running tasks. Alice Labs defaults to JSON-RPC over HTTPS with SSE for enterprise deployments because it maps cleanly to existing API gateways." } }, { "@type": "Question", "name": "Does Microsoft Agent Framework support A2A?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Microsoft shipped A2A v1 support in the Agent Framework for .NET on April 28, 2026, with Python parity on July 30, 2026. Two package roles exist: A2A Agent (client-side) and A2A Hosting (server-side). Alice Labs uses MAF's AddA2AServer() plus MapA2AHttpJson() to expose Foundry agents as A2A endpoints, and A2ACardResolver on the client side to consume remote agents as standard AIAgent instances." } }, { "@type": "Question", "name": "Does LangGraph support A2A?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. LangSmith Agent Server exposes A2A endpoints at /a2a/{assistant_id}, letting LangGraph orchestrators be called by any A2A-compliant client. On the client side, LangGraph orchestrators can query a registry, match capability tags on remote Agent Cards, and delegate subtasks via JSON-RPC 2.0. Native remote-A2A-agent-as-sub-graph is still an open community feature request." } }, { "@type": "Question", "name": "Does CrewAI support A2A?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. CrewAI ships A2A adapters so one Crew can be exposed as a standardized endpoint that another Crew or any A2A-compliant client can call. This lets a CrewAI-based specialist team be composed into a larger multi-framework mesh without leaking CrewAI-specific implementation details." } }, { "@type": "Question", "name": "Which authentication schemes does A2A support?", "acceptedAnswer": { "@type": "Answer", "text": "A2A supports six authentication schemes: APIKey, HTTP Basic, HTTP Bearer, OAuth 2.0 (authorization code, client credentials, and device code flows), OpenID Connect, and mutual TLS. Schemes are advertised in the Agent Card but not enforced by the protocol — credential acquisition is delegated to implementers. Alice Labs' default for enterprise cross-boundary deployments is OpenID Connect plus mTLS." } }, { "@type": "Question", "name": "What are the main security risks of A2A?", "acceptedAnswer": { "@type": "Answer", "text": "Named risks include agent impersonation, Agent Card tampering, replay attacks, webhook SSRF, and context poisoning of Agent Cards. The card-refresh timing gap can expose clients to compromised cards until they refetch. Mitigations: enforce signed Agent Cards, pin trusted issuers, validate JWS on every fetch, gate webhook targets by allowlist, and log every Task state transition to your SIEM." } }, { "@type": "Question", "name": "What is AP2 and how does it relate to A2A?", "acceptedAnswer": { "@type": "Answer", "text": "The Agent Payments Protocol (AP2) launched in September 2025 with Google and Coinbase, and had 60+ payments and finserv partners by April 2026. It layers cryptographic payment mandates on top of A2A so agents can autonomously execute payments with verifiable trust. The x402 extension, co-developed with Coinbase and MetaMask, carries crypto payment authority alongside traditional card rails." } }, { "@type": "Question", "name": "Can I use A2A across AWS, Azure, and Google Cloud?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. GA A2A support ships inside Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock AgentCore Runtime, and Google Vertex AI Agent Builder as of April 2026. Cross-cloud A2A calls are plain HTTPS, so standard cloud networking (private endpoints, VPC peering, egress rules, API gateways) still applies. Alice Labs has shipped multi-cloud A2A orchestrations across all three major clouds." } }, { "@type": "Question", "name": "When should I use A2A vs in-process agent composition?", "acceptedAnswer": { "@type": "Answer", "text": "Use A2A when agents cross a service, team, cloud, or organizational boundary. Skip A2A when all agents live in the same process and are owned by the same team — agents-as-tools has less overhead (microseconds vs 10–100ms of HTTP latency per call). Alice Labs' rule of thumb: A2A between crews, agents-as-tools inside a crew." } }, { "@type": "Question", "name": "How does A2A handle long-running tasks?", "acceptedAnswer": { "@type": "Answer", "text": "A2A Tasks progress through explicit lifecycle states (SUBMITTED, WORKING, INPUT_REQUIRED, AUTH_REQUIRED, COMPLETED, FAILED, CANCELED, REJECTED). Server-Sent Events (SSE) push incremental updates over a single long-lived HTTP response. For Tasks that outlive a client connection, webhooks let the agent POST completion notifications to a caller-registered URL. Remote agents own their conversation state, keyed by context ID." } }, { "@type": "Question", "name": "Is A2A a competitor to OpenAI's function calling?", "acceptedAnswer": { "@type": "Answer", "text": "No — different problem. OpenAI function calling is a model-level feature that lets a single model call tools during generation. A2A is a network protocol that lets agents call other agents across process, team, cloud, or organizational boundaries. Function calling maps to what MCP standardizes at the tool layer. A2A sits above that, at the agent-to-agent coordination layer." } }, { "@type": "Question", "name": "What SDKs are available for A2A in 2026?", "acceptedAnswer": { "@type": "Answer", "text": "As of Q2 2026, five production-ready SDKs are available: Python, JavaScript, Java, Go, and .NET. That covers essentially every enterprise runtime an AI team is likely to standardize on. Because A2A is plain HTTP, any HTTP-capable runtime can implement it without an SDK — several teams have shipped A2A agents in Rust and Elixir with no framework at all." } }, { "@type": "Question", "name": "How does A2A support EU AI Act compliance?", "acceptedAnswer": { "@type": "Answer", "text": "A2A's explicit Task lifecycle states and Agent Card discovery model make the record-keeping obligations under EU AI Act Article 12 tractable: log every state transition with caller identity, Agent Card issuer, and Task ID to your SIEM. Combined with mTLS + OpenID Connect on the ingress and signed Agent Cards on discovery, A2A meshes can satisfy the traceability and human-oversight requirements for high-risk AI systems. Always confirm scope with legal counsel." } }, { "@type": "Question", "name": "How do I get started building an A2A agent?", "acceptedAnswer": { "@type": "Answer", "text": "For a MAF .NET agent: services.AddA2AServer(agent), then app.MapA2AHttpJson('/a2a') and app.MapWellKnownAgentCard(). For LangGraph: deploy to LangSmith Agent Server and use the auto-exposed /a2a/{assistant_id} endpoint. For any HTTP runtime: publish a JSON Agent Card at /.well-known/agent-card.json and accept JSON-RPC 2.0 Task requests. Alice Labs recommends starting with signed Agent Cards from day one — it is trivial to add up front and painful to retrofit." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "A2A Protocol Guide 2026: Cross-Cloud Agent Communication for Enterprise", "description": "A2A protocol guide: v1.0 spec, Agent Cards, JSON-RPC and gRPC transports, A2A vs MCP, and enterprise deployment patterns across Azure, AWS Bedrock, and Vertex.", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026", "datePublished": "2026-08-02", "dateModified": "2026-08-02", "creator": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "license": "https://creativecommons.org/licenses/by/4.0/", "isAccessibleForFree": true, "keywords": [ "a2a protocol", "agent to agent protocol", "a2a spec", "a2a vs mcp", "cross cloud agent communication", "agent interoperability", "agent cards", "a2a v1" ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Related articles", "itemListElement": [ { "@type": "ListItem", "position": 1, "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026", "name": "Best AI Agent Frameworks 2026: The Enterprise Comparison" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/langgraph-guide-2026", "name": "LangGraph Tutorial 2026: Build Stateful AI Agents for Enterprise" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/crewai-guide-2026", "name": "CrewAI Guide 2026: Role-Based Multi-Agent Systems" }, { "@type": "ListItem", "position": 4, "url": "https://alicelabs.ai/en/insights/multi-agent-systems-explained", "name": "Multi-Agent Systems Explained: Patterns and Trade-offs" }, { "@type": "ListItem", "position": 5, "url": "https://alicelabs.ai/en/insights/ai-agent-orchestration", "name": "AI Agent Orchestration: Patterns for Enterprise" }, { "@type": "ListItem", "position": 6, "url": "https://alicelabs.ai/en/insights/ai-agent-security-risks", "name": "AI Agent Security Risks" }, { "@type": "ListItem", "position": 7, "url": "https://alicelabs.ai/en/insights/eu-ai-act-compliance-checklist-2026", "name": "EU AI Act Compliance Checklist for 2026" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 15, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "What is the A2A (Agent-to-Agent) protocol?", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#what-is-a2a-protocol" }, { "@type": "ListItem", "position": 2, "name": "A2A spec v1.0: core objects and lifecycle", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-spec-core-objects" }, { "@type": "ListItem", "position": 3, "name": "Agent Cards: how agents discover each other", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#agent-cards-discovery" }, { "@type": "ListItem", "position": 4, "name": "A2A transport methods: JSON-RPC, gRPC, and REST", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-transports" }, { "@type": "ListItem", "position": 5, "name": "A2A vs MCP: two protocols, two layers", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-vs-mcp" }, { "@type": "ListItem", "position": 6, "name": "Cross-cloud agent communication with A2A", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#cross-cloud-agent-communication" }, { "@type": "ListItem", "position": 7, "name": "A2A in Microsoft Agent Framework (.NET and Python)", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-microsoft-agent-framework" }, { "@type": "ListItem", "position": 8, "name": "A2A in LangGraph and Google ADK", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-langgraph-adk" }, { "@type": "ListItem", "position": 9, "name": "A2A in CrewAI and other frameworks", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-crewai-other-frameworks" }, { "@type": "ListItem", "position": 10, "name": "A2A authentication and security schemes", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-auth-schemes" }, { "@type": "ListItem", "position": 11, "name": "A2A security risks and mitigations", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-security-risks" }, { "@type": "ListItem", "position": 12, "name": "A2A enterprise use cases in production (2026)", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-enterprise-use-cases" }, { "@type": "ListItem", "position": 13, "name": "Agent Payments Protocol (AP2) and the x402 extension", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#ap2-x402-payments" }, { "@type": "ListItem", "position": 14, "name": "When to use A2A vs in-process agent composition", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#a2a-vs-in-process" }, { "@type": "ListItem", "position": 15, "name": "Alice Labs A2A implementation playbook", "url": "https://alicelabs.ai/en/insights/a2a-protocol-guide-2026#alice-labs-a2a-playbook" } ] } ] }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "AI Agents", "item": "https://alicelabs.ai/en/insights/ai-agents" }, { "@type": "ListItem", "position": 4, "name": "A2A Protocol Guide 2026: Cross-Cloud Agent Communication for Enterprise" } ] } ] --- [Alice Labs](/en/) Services [ What we do ](/#welcome)[ About Alice ](/#who-we-are)[ Case ](/en/case)[ Insights ](/en/insights)[ Contact ](/#email-form) 1. [Home](/en) [Insights](/en/insights) [AI Agents](/en/insights/ai-agents) A2A Protocol Guide 2026: Cross-Cloud Agent Communication for Enterprise AI Agents Deep Dive Fresh · Last reviewed: 2 August 2026 · 44d ago # A2A Protocol Guide 2026: Cross-Cloud Agent Communication for Enterprise ## TL;DR Quick Answer Cited by AI > A2A is the open Linux Foundation protocol for cross-agent communication. Reached v1.0 in April 2026, uses JSON-RPC 2.0, gRPC, or REST over HTTP, and complements MCP: A2A connects agents to agents, MCP connects agents to tools. The Agent-to-Agent (A2A) protocol reached v1.0 stable in April 2026. This guide covers the spec, Agent Cards, transports, A2A vs MCP, framework support in Microsoft Agent Framework, LangGraph, Google ADK, and CrewAI, plus Alice Labs' production deployment playbook. The Agent-to-Agent (A2A) protocol is an open, HTTP-based standard governed by the Linux Foundation that lets AI agents built on different frameworks, clouds, or vendors discover each other, authenticate, and delegate tasks. It reached v1.0 in April 2026 with 150+ supporting organizations. ![Eric Lundberg - Author at Alice Labs](/images/eric-lundberg.png) Written by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) ![Linus Ingemarsson - Reviewer at Alice Labs](/images/linus-ingemarsson.png) Reviewed by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) Published August 2, 2026 16 min read v1.0 A2A stable release, April 2026 (one year after launch) [Linux Foundation](https://www.linuxfoundation.org/press/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year) 150+ Organizations supporting the Agent2Agent Project at Linux Foundation [Linux Foundation press release, April 2026](https://www.linuxfoundation.org/press/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year) 100+ Production AI implementations delivered by Alice Labs since 2023 [Alice Labs internal data](https://alicelabs.ai/en/ai-agents) What you'll learn(8 points) - What the A2A protocol is, its origin at Google Cloud, and how the Linux Foundation now governs it - The five core objects in the v1.0 spec: Task, Message, Part, Artifact, and Agent Card - How Agent Cards enable discovery, and how Signed Agent Cards (JWS) verify identity - The three transport bindings — JSON-RPC 2.0, gRPC, and HTTP+JSON/REST — and when to pick each - How A2A differs from MCP, and why most production stacks use both together - How A2A works inside Microsoft Agent Framework, LangGraph, Google ADK, and CrewAI - The named security risks in A2A meshes and the mitigations that ship with v1.0 - Alice Labs' production A2A playbook based on 100+ enterprise AI implementations ## Key Takeaways - 01 A2A hit v1.0 stable in April 2026, one year after Google Cloud's April 2025 launch — with 150+ supporting organizations and 22,000+ GitHub stars. - 02 A2A and MCP solve different layers: MCP standardizes agent-to-tool access (97M monthly SDK downloads by April 2026); A2A standardizes agent-to-agent delegation across boundaries. - 03 GA support ships inside Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock AgentCore Runtime, and Google Vertex AI Agent Builder — cross-cloud A2A is production-ready. - 04 The Technical Steering Committee includes AWS, Cisco, Google, IBM Research, Microsoft, Salesforce, SAP, and ServiceNow — genuine vendor-neutral governance under the Linux Foundation. - 05 Agent Cards are JSON documents served at /.well-known/agent-card.json; v1.0 introduced Signed Agent Cards using JSON Web Signature for cryptographic identity verification. - 06 Six authentication schemes are supported (APIKey, HTTP Basic, HTTP Bearer, OAuth 2.0, OpenID Connect, mTLS) — but the protocol advertises them, it does not enforce them; hardening is the implementer's job. - 07 The Agent Payments Protocol (AP2) and its x402 crypto extension layer autonomous payments on top of A2A, with 60+ payments and finserv partners at launch. - 08 Alice Labs' rule of thumb across 100+ implementations: A2A between crews, agents-as-tools inside a crew — do not add HTTP hops for agents in the same process. ### Contents 16 min left - [01 What is the A2A (Agent-to-Agent) protocol? ](#what-is-a2a-protocol) - [02 A2A spec v1.0: core objects and lifecycle ](#a2a-spec-core-objects) - [03 Agent Cards: how agents discover each other ](#agent-cards-discovery) - [04 A2A transport methods: JSON-RPC, gRPC, and REST ](#a2a-transports) - [05 A2A vs MCP: two protocols, two layers ](#a2a-vs-mcp) - [06 Cross-cloud agent communication with A2A ](#cross-cloud-agent-communication) - [07 A2A in Microsoft Agent Framework (.NET and Python) ](#a2a-microsoft-agent-framework) - [08 A2A in LangGraph and Google ADK ](#a2a-langgraph-adk) - [09 A2A in CrewAI and other frameworks ](#a2a-crewai-other-frameworks) - [10 A2A authentication and security schemes ](#a2a-auth-schemes) - [11 A2A security risks and mitigations ](#a2a-security-risks) - [12 A2A enterprise use cases in production (2026) ](#a2a-enterprise-use-cases) - [13 Agent Payments Protocol (AP2) and the x402 extension ](#ap2-x402-payments) - [14 When to use A2A vs in-process agent composition ](#a2a-vs-in-process) - [15 Alice Labs A2A implementation playbook ](#alice-labs-a2a-playbook) Part of [Best AI Agent Frameworks 2026: The Enterprise Comparison](/en/insights/best-ai-agent-frameworks-2026) 01 / 15 Chapter ## What is the A2A (Agent-to-Agent) protocol? A2A is an open, HTTP-based protocol that lets AI agents built on different frameworks, clouds, or vendors discover each other, authenticate, and delegate tasks. It launched at Google Cloud in April 2025, moved to the Linux Foundation in June 2025, and reached v1.0 in April 2026. The Agent-to-Agent (A2A) protocol was introduced by Google Cloud in April 2025 with [50+ enterprise launch partners](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/) — Salesforce, SAP, Accenture, Deloitte, and a long tail of systems integrators and ISVs. In June 2025, Google donated the protocol, specification, SDKs, and reference tooling to the Linux Foundation, which established the Agent2Agent Project for vendor-neutral governance. The v1.0.0 stable specification was released in April 2026, one year after launch. It is published at [a2a-protocol.org](https://a2a-protocol.org/latest/specification/) and is the canonical source of truth for every implementation. Alice Labs treats the Linux Foundation spec — not any vendor's SDK documentation — as the primary reference for client work. Mechanically, A2A is an HTTP protocol with three transport bindings — JSON-RPC 2.0, gRPC, and HTTP+JSON/REST — plus Server-Sent Events (SSE) for streaming updates and webhooks for push notifications on long-running tasks. There is no proprietary wire format. Any HTTP-capable runtime can implement it. The critical distinction most enterprise teams get wrong: A2A is not a replacement for the Model Context Protocol (MCP). A2A standardizes how agents communicate with _other agents_. MCP standardizes how a single agent communicates with _tools, data, and APIs_. Both layers are typically used together in production — covered in detail in the A2A vs MCP section below. For a broader survey of the frameworks A2A connects, see our [best AI agent frameworks guide for 2026](/en/insights/best-ai-agent-frameworks-2026) — every framework in that comparison now ships some form of A2A support. A2A vs MCP in one sentence MCP is how one agent reaches its own tools. A2A is how one agent reaches another agent's endpoint across a service, team, cloud, or organizational boundary. April 2025 A2A protocol launch at Google Cloud with 50+ partners [Google Cloud Developers Blog](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/) April 2026 A2A v1.0.0 stable specification released [a2a-protocol.org](https://a2a-protocol.org/latest/specification/) 02 / 15 Chapter ## A2A spec v1.0: core objects and lifecycle In short The A2A v1.0 spec defines five core objects — Task, Message, Part, Artifact, and Agent Card — and eight Task lifecycle states. Three transport bindings maintain functionally equivalent representations of the same data model. The A2A v1.0 data model is deliberately small. Five core objects, one lifecycle state machine, and one versioning header. That minimalism is what makes cross-framework interoperability tractable. The five core objects: - **Task:** The unit of work an agent performs. Every A2A interaction is a Task with a state, inputs, and outputs. - **Message:** A single turn in the conversation between client and agent. Carries a `role` (USER or AGENT) and an array of Parts. - **Part:** The atomic content unit inside a Message — text, bytes (files), URL references, or structured JSON. - **Artifact:** A durable output produced by a Task — a report, a structured record, an image, or any file the agent generates. - **Agent Card:** The JSON discovery document that declares an agent's identity, endpoint, skills, and supported authentication schemes. Task lifecycle states are strict: `SUBMITTED → WORKING → INPUT_REQUIRED → AUTH_REQUIRED → COMPLETED / FAILED / CANCELED / REJECTED`. State transitions are the observability surface — every production A2A mesh Alice Labs has deployed logs every transition to an OpenTelemetry-compatible backend. Versioning uses an `A2A-Version` HTTP header in Major.Minor format. v1.0 removed the "kind discriminator" field, relocated several extended card fields, and made other breaking changes documented in Appendix A of the specification's migration guide. Teams upgrading from 0.2.x or 0.3.x should walk that guide before pushing v1.0 to production. All three transport bindings — JSON-RPC 2.0, gRPC, and HTTP+JSON/REST — carry the same Task/Message/Part/Artifact model. The spec calls them "functionally equivalent" and that equivalence is enforced by conformance tests: an Artifact produced by a gRPC agent round-trips cleanly through a JSON-RPC client. A2A v1.0 Task Lifecycle States State Meaning Typical trigger SUBMITTED Task accepted, not yet started Client posts initial Message WORKING Agent is actively processing Agent begins execution INPUT\_REQUIRED Agent needs more information from caller Missing parameter or clarification AUTH\_REQUIRED Agent needs credentials before it can proceed Delegated OAuth flow or scope escalation COMPLETED Successful terminal state Artifacts emitted, Task closes FAILED Terminal error state Unrecoverable error inside the agent CANCELED Terminal state, caller cancelled Client sends cancel request REJECTED Terminal state, agent refused Task Policy denial or unsupported capability Migrating from 0.x to v1.0 v1.0 removed the kind discriminator and relocated extended Agent Card fields. Walk Appendix A of the migration guide before upgrading production agents — silent field drops are the most common regression Alice Labs sees during v1.0 rollouts. 03 / 15 Chapter ## Agent Cards: how agents discover each other In short Agent Cards are JSON documents published at /.well-known/agent-card.json that declare an agent's identity, endpoint, skills, and auth schemes. v1.0 introduced Signed Agent Cards using JSON Web Signature (JWS) for cryptographic identity verification. Discovery is the feature that makes A2A worth adopting. Without it, every integration is still a bespoke contract negotiated by humans. With it, an orchestrator can query a registry, retrieve capability metadata, and delegate work at runtime — no code change required. An Agent Card is a JSON document served at a well-known path — canonically `/.well-known/agent-card.json` on the agent's own host. It declares: - Identity — human-readable name, description, provider org - Endpoint URL — where clients POST Task requests - Skills — capability tags an orchestrator can match against - Input/output modalities — text, files, structured JSON, streaming - Supported authentication schemes — advertised, not enforced - Extensions — optional protocol extensions the agent implements v1.0 introduced **Signed Agent Cards** using JSON Web Signature (JWS). A signed card carries a detached JWS that a client can verify against a known issuer public key. This closes a class of impersonation attacks that plagued 0.x implementations — an attacker could previously host a card claiming to be another agent's endpoint, and clients had no cryptographic recourse. Signing is _supported_ but not _enforced_ by the protocol. The spec explicitly leaves signing enforcement to implementers. Alice Labs' default posture for enterprise deployments: unsigned Agent Cards are acceptable only in a single-team development environment; production and staging require signed cards with a pinned trusted-issuer list. Registries — separate infrastructure, not part of the core protocol — let orchestrators list many agents, query by capability tag, and receive freshness/health signals. Several cloud vendors ship first-party registries (Vertex AI Agent Builder, Azure AI Foundry), and open-source registries exist for self-hosted meshes. Always publish signed cards in production The two lines of infrastructure work required to sign your Agent Card — JWS key generation and a resolver that verifies signatures — close every documented impersonation attack against A2A meshes. Do it on day one, not after a security incident. 04 / 15 Chapter ## A2A transport methods: JSON-RPC, gRPC, and REST In short A2A v1.0 defines three transport bindings: JSON-RPC 2.0 over HTTP or WebSocket (reference), gRPC (typed streaming), and HTTP+JSON/REST. All three carry the same Task and Artifact model. SSE handles streaming; webhooks handle push notifications. The three transport bindings exist because enterprise teams have different existing infrastructure. Forcing a choice between JSON-RPC and REST would have blocked adoption in shops that had standardized on one or the other. The spec's promise of functional equivalence — the same Task round-trips through each transport — is what makes multi-transport meshes viable. A2A v1.0 Transport Bindings Compared Binding Best for Streaming Typical stack fit JSON-RPC 2.0 over HTTP/WS Default choice; maps to existing API gateways SSE or WebSocket Web services, Node, Python, .NET behind a reverse proxy gRPC High-throughput, typed service definitions Native bidirectional streaming Internal microservice meshes, Go/Rust/Java shops HTTP+JSON/REST Teams that avoid RPC frameworks entirely SSE + webhooks Conventional REST APIs, OpenAPI-first orgs Server-Sent Events (SSE) is the default streaming mechanism — a Task in the WORKING state can push incremental updates (partial Artifacts, thought traces, tool-call results) to the caller over a single long-lived HTTP response. For long-running Tasks that outlive a client connection, webhooks let the agent POST completion notifications to a caller-registered URL. Alice Labs' default for enterprise deployments is JSON-RPC 2.0 over HTTPS with SSE for streaming. The reason is boring but decisive: it drops into existing API gateway, authentication, and observability stacks without any custom infrastructure. gRPC becomes the right choice when the agent is inside a service mesh (Istio, Linkerd) that already carries other gRPC traffic. 05 / 15 Chapter ## A2A vs MCP: two protocols, two layers In short MCP connects one agent to tools, databases, and APIs. A2A connects multiple agents so they can delegate tasks to each other across boundaries. Production multi-agent systems typically use both: MCP inside each agent for tool access, A2A between agents for coordination. The most common architectural mistake Alice Labs encounters during A2A adoption reviews: teams treat A2A and MCP as competing standards and pick one. They solve different layers. Picking one leaves half the stack unstandardized. A2A vs MCP: What Each Standardizes Dimension A2A MCP What it connects Agent ↔ agent Agent ↔ tools, data, APIs Governance Linux Foundation (donated by Google) MCP working group (originated at Anthropic) Discovery Agent Card at /.well-known/agent-card.json MCP server capability negotiation Transport JSON-RPC 2.0, gRPC, REST over HTTP JSON-RPC over stdio, HTTP, SSE Adoption signal (April 2026) 150+ orgs, 22,000+ GitHub stars 97M monthly SDK downloads (970x growth in 16 months) When to use Delegation across service, team, cloud, or org boundaries Giving one agent access to tools without custom code The reference pattern shipped in every A2A launch talk since April 2025: a retail inventory agent uses MCP to check stock in its own warehouse database, then uses A2A to delegate a purchase order to a supplier's remote agent. MCP handles the internal tool call. A2A crosses the organizational boundary. The two protocols also compose cleanly. Every agent exposed over A2A should have its own MCP-based tool belt on the inside — that way, callers do not need to know anything about the internal tools, and internal tools can be swapped without breaking the external contract. For a deeper walk-through of the framework matrix that consumes both protocols, see our [best AI agent frameworks guide for 2026](/en/insights/best-ai-agent-frameworks-2026). MCP scale as of April 2026 MCP hit 97M monthly SDK downloads by April 2026 — 970x growth in 16 months. A2A hit 150+ supporting organizations and 22,000+ GitHub stars in the same window. Both surfaces are past the point of experimental adoption. 06 / 15 Chapter ## Cross-cloud agent communication with A2A In short A2A has GA support inside Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock AgentCore Runtime, and Google Vertex AI Agent Builder. Cross-cloud calls are plain HTTPS, so existing cloud networking controls — private endpoints, VPC peering, egress rules — still apply. The enterprise pain A2A actually solves: an agent built on one cloud's stack cannot easily call an agent built on another cloud's stack. Every pair of vendors had a different way to expose agents, a different auth model, and a different way to stream partial results. That N-squared integration problem is what the Linux Foundation's April 2026 update explicitly targeted. As of April 2026, GA support ships inside: - **Microsoft Copilot Studio** and **Azure AI Foundry** — both outbound (call remote A2A agents as tools) and incoming (expose Foundry agents as A2A endpoints, public preview announced at Microsoft Build 2026). - **Amazon Bedrock AgentCore Runtime** — first-party A2A hosting for agents deployed on Bedrock. - **Google Vertex AI Agent Builder** — native A2A hosting plus client SDKs (Google's original launch surface). The Technical Steering Committee of the Agent2Agent Project includes AWS, Cisco, Google, IBM Research, Microsoft, Salesforce, SAP, and ServiceNow. Governance is genuinely vendor-neutral — no single cloud can unilaterally change the wire format. Cross-cloud calls remain plain HTTP requests. That is deliberate and important: standard cloud networking still applies. Private endpoints, VPC peering, egress rules, WAF policies, and API gateways all work exactly as they do for any other HTTP service. A2A does not require any new network primitive. Alice Labs has shipped 100+ production AI implementations including multi-cloud A2A orchestrations that span Azure, AWS Bedrock, and Google Vertex — typically with a LangGraph orchestrator on one cloud delegating to specialist agents on the others. The engineering pattern is straightforward once the security controls are set: mTLS + OIDC on the ingress, signed Agent Cards on discovery, OpenTelemetry on every Task state transition. The vendor list matters for procurement When an enterprise procurement team asks 'is this a standard or a Google thing?', the answer is: the Technical Steering Committee includes AWS, Cisco, Google, IBM Research, Microsoft, Salesforce, SAP, and ServiceNow. That plurality is what lets a Bedrock agent and a Vertex agent talk to each other without either vendor's SDK. 07 / 15 Chapter ## A2A in Microsoft Agent Framework (.NET and Python) In short Microsoft shipped A2A v1 support in the Microsoft Agent Framework for .NET on April 28, 2026, with Python parity in agent-framework-a2a on July 30, 2026. Two package roles exist: A2A Agent (client-side) and A2A Hosting (server-side). Microsoft Agent Framework (MAF) was one of the fastest movers on A2A v1. The .NET SDK landed A2A v1 support on April 28, 2026 — days after the protocol went stable — and the Python SDK reached parity on July 30, 2026. MAF splits A2A into two package roles you compose depending on which side of the wire you are on: - **A2A Agent (client-side):** Lets a MAF orchestrator consume a remote A2A agent as if it were any other `AIAgent` instance. The client uses `A2ACardResolver` to fetch and verify the remote Agent Card, then invokes Tasks against the returned agent. - **A2A Hosting (server-side):** Exposes a MAF agent as an A2A endpoint that any A2A-compliant client can call. The .NET pattern is `services.AddA2AServer(agent)` then `app.MapA2AHttpJson("/a2a")` and `app.MapWellKnownAgentCard()`. The Foundry integration goes further. Azure AI Foundry supports both outbound A2A (Foundry agents calling remote A2A agents as tools) and incoming A2A (Foundry agents exposed as A2A endpoints, in public preview at Microsoft Build 2026). This is the surface Alice Labs uses most often for Nordic enterprise clients — Foundry provides the managed hosting, MAF provides the code, A2A provides the wire. A minimal .NET hosting example — the shape of code MAF ships in its A2A samples: // ASP.NET Core — expose a MAF agent as an A2A endpoint var builder = WebApplication.CreateBuilder(args); builder.Services.AddA2AServer(agent); // registers the A2A hosting layer var app = builder.Build(); app.MapA2AHttpJson("/a2a"); // JSON-RPC 2.0 endpoint app.MapWellKnownAgentCard(); // /.well-known/agent-card.json app.Run(); On the client side, consuming a remote A2A agent is a two-step: resolve the card, invoke: // Client-side — consume a remote A2A agent as an AIAgent var resolver = new A2ACardResolver("https://partner.example.com"); AIAgent remote = await resolver.GetAgentAsync(); var response = await remote.RunAsync("Draft a supplier purchase order for SKU 1029"); For a deeper look at how MAF composes agents into full applications — before you get to A2A — see our [best AI agent frameworks guide for 2026](/en/insights/best-ai-agent-frameworks-2026) and the surrounding framework deep-dives. Foundry incoming A2A is still preview As of Microsoft Build 2026, exposing Foundry-hosted agents as incoming A2A endpoints is public preview — GA is expected but not yet dated. Client-side outbound A2A from Foundry is GA today. 08 / 15 Chapter ## A2A in LangGraph and Google ADK In short LangSmith Agent Server exposes A2A at /a2a/{assistant\_id} out of the box. Google ADK ships first-party A2A hosting and is the reference implementation for streaming Task updates. LangGraph orchestrators can delegate subtasks to peer A2A agents via a registry lookup and JSON-RPC 2.0 call. LangGraph and Google ADK — the two most-used orchestration frameworks alongside Microsoft Agent Framework — both ship first-party A2A support. Between them and MAF, almost any polyglot enterprise mesh is buildable without custom protocol code. **LangGraph.** LangSmith Agent Server exposes an A2A endpoint at `/a2a/{assistant_id}` for every deployed LangGraph assistant. That means any A2A-compliant client can call a LangGraph agent without knowing anything about LangGraph itself. On the orchestrator side, a LangGraph node can query a registry, match a Task against capability tags in remote Agent Cards, and invoke the winning agent via JSON-RPC 2.0. A community feature request is open for making remote A2A agents first-class LangGraph sub-graphs (with checkpointing and state sharing across the wire), but that is not yet native. For the LangGraph fundamentals A2A sits on top of, see our [LangGraph tutorial for 2026](/en/insights/langgraph-guide-2026). **Google ADK.** The Google Agent Development Kit ships first-party A2A hosting and is the reference implementation Google's own launch demos use for streaming Task updates. ADK research agents were the canonical example at Google Cloud Next 2025 and remain the highest-quality reference for SSE-based partial-result streaming over A2A. A reference multi-framework topology Alice Labs has deployed for European clients: - **LangGraph orchestrator** hosted on Azure — receives the user request, decides which specialist to invoke, checkpoints state to PostgreSQL. - **Google ADK research worker** hosted on Vertex — invoked over A2A when the request needs deep web research; streams partial findings back via SSE. - **MAF domain worker** hosted on AWS Bedrock — invoked over A2A when the request needs domain-specific action inside a Microsoft-centric enterprise system. All three communicate over A2A v1.0 with signed Agent Cards and mTLS. No custom protocol code exists anywhere in the mesh — every hop uses framework-native A2A packages. 09 / 15 Chapter ## A2A in CrewAI and other frameworks In short CrewAI ships A2A adapters so one Crew can be exposed as a standardized endpoint. Semantic Kernel, .NET code, CrewAI, and commercial CRM agents (Salesforce Agentforce, ServiceNow AI Agents) all interoperate over A2A. SDK coverage as of Q2 2026: Python, JavaScript, Java, Go, and .NET. Beyond MAF, LangGraph, and ADK, the framework matrix that speaks A2A has expanded significantly in the year since launch. As of Q2 2026 the picture is: - **CrewAI** — first-party A2A adapters. One Crew can be exposed as a standardized A2A endpoint that another Crew (or any other framework) can call. See our [CrewAI guide for 2026](/en/insights/crewai-guide-2026) for role-based agent patterns that pair well with A2A across-Crew delegation. - **Semantic Kernel** and custom **.NET code** — via the same MAF-adjacent A2A packages Microsoft ships. - **IBM watsonx.ai** — A2A adapters for LangGraph-backed agents. - **Salesforce Agentforce** and **ServiceNow AI Agents** — commercial CRM and ITSM agents exposing A2A endpoints for enterprise integration. SDK coverage across languages is now production-grade in five: Python, JavaScript, Java, Go, and .NET. That covers essentially every enterprise runtime an AI team is likely to standardize on. The important architectural point: _any HTTP-capable runtime can implement A2A_. There is no framework lock-in. A team could write an A2A agent in Rust with no framework at all, and a LangGraph orchestrator would call it exactly the same way as any other A2A endpoint. This is the level of freedom that distinguishes A2A from earlier, vendor-controlled agent APIs. For teams still choosing which orchestration framework to standardize on — before worrying about A2A between them — start with our [best AI agent frameworks guide for 2026](/en/insights/best-ai-agent-frameworks-2026) and the [LangGraph vs CrewAI vs AutoGen comparison](/en/insights/langgraph-vs-crewai-vs-autogen). ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. [Book a Discovery Call](#contact) 10 / 15 Chapter ## A2A authentication and security schemes In short A2A supports six auth schemes: APIKey, HTTP Basic, HTTP Bearer, OAuth 2.0 (three flows), OpenID Connect, and mutual TLS. Schemes are advertised in the Agent Card but not enforced by the protocol — credential acquisition is delegated to implementers via out-of-band flows. Security in A2A is a discoverable contract, not an enforced mandate. The Agent Card declares which schemes an agent supports; the protocol does not mandate that the agent actually verifies them. That means implementer discipline determines whether an A2A mesh is safe. The six supported authentication schemes: - **APIKey** — simple bearer token, minimum bar for dev/staging - **HTTP Basic** — legacy, discouraged for new deployments - **HTTP Bearer** — bearer token in Authorization header - **OAuth 2.0** — three flows supported: authorization code, client credentials, device code - **OpenID Connect** — identity layer on top of OAuth 2.0 - **Mutual TLS (mTLS)** — certificate-based mutual authentication, highest bar The Agent Card exposes supported schemes via `AgentCard.securitySchemes` and required scopes via `AgentCard.security`. Clients read those fields, obtain credentials via the appropriate out-of-band process (OAuth authorization redirect, mTLS certificate issuance, etc.), and present them on Task submission. Alice Labs' default recommendation for enterprise deployments crossing organizational boundaries: **OpenID Connect for user identity plus mTLS for service identity**. OIDC handles delegated user authorization (the human's consent flows through to the downstream agent). mTLS handles machine-to-machine trust (the calling service proves it is who it says it is at the transport layer). Together, they satisfy the auth requirements of every regulated Nordic and European client we have shipped for. Advertised is not the same as enforced An Agent Card listing OAuth 2.0 as a supported scheme does not mean the agent actually validates OAuth tokens. Verify enforcement with a test call before trusting a partner agent's card — this is a real class of bugs in early A2A deployments. 11 / 15 Chapter ## A2A security risks and mitigations In short Named A2A risks include agent impersonation, Agent Card tampering, replay attacks, webhook SSRF, and context poisoning. Mitigations: enforce signed Agent Cards, pin trusted issuers, validate JWS on every fetch, gate webhook targets by allowlist, and log every Task state transition. The security literature on A2A matured quickly once v1.0 shipped. Palo Alto Networks and Tyk both published threat models in 2026 that name the specific classes of attack an orchestrator must control for. Every one of these is addressable — but only if the controls are designed in from day one. A2A Security Risks and Mitigations Risk What it looks like Mitigation Agent impersonation Attacker hosts a card claiming to be another agent Enforce Signed Agent Cards; pin trusted issuers Card tampering Card modified in transit or at rest to redirect endpoints Validate JWS on every fetch; do not cache unsigned cards Card-refresh timing gap Compromised card exposes clients until they refetch Short TTLs on card cache; issuer revocation lists Replay attacks Attacker resubmits a captured Task with new intent Nonce + timestamp per request; short-lived bearer tokens Webhook SSRF Malicious webhook target routes requests to internal services Allowlist webhook hosts; block private IP ranges Context poisoning Malicious content in Agent Card manipulates orchestrator LLM Treat card fields as untrusted input; validate against schema Role fluidity Client and server roles swap mid-conversation, enabling impersonation Bind identity to mTLS certificate, not to role in the message McKinsey's 2025 framing of the five agent risks — uncontrolled autonomy, fragmented access, low observability, expanding attack surface, and agent sprawl — all apply to A2A meshes at scale. A registry of 30+ agents with untracked owners is where these risks compound. For the broader security posture that A2A slots into, see our [AI agent security risks guide](/en/insights/ai-agent-security-risks). Log every Task state transition Every state change — SUBMITTED, WORKING, INPUT\_REQUIRED, COMPLETED, FAILED — should be logged to your SIEM with the caller identity, Agent Card issuer, and Task ID. That log is the audit trail regulators will ask for. OpenTelemetry Agent semconv is stabilizing in 2026 and is the recommended shape. 12 / 15 Chapter ## A2A enterprise use cases in production (2026) In short The Linux Foundation reports active A2A production deployments in supply chain, financial services, insurance, and IT operations as of April 2026. Common patterns: cross-organizational purchase orders, claims triage delegation, incident triage across cloud boundaries, and 60+ payments partners backing the AP2 layer. The Linux Foundation's one-year update in April 2026 named four verticals with active A2A production deployments: supply chain, financial services, insurance, and IT operations. Each has settled on a recognizable pattern. A2A Production Patterns by Vertical (April 2026) Vertical Pattern Why A2A specifically Supply chain Cross-org purchase-order delegation between retailer and supplier agents Crosses organizational boundary Financial services Agent-initiated payments via AP2 + x402 (60+ payments partners) Cryptographic mandates require standardized wire Insurance Claims-triage delegating to document-processing and fraud-detection agents Signed endpoints required for third-party trust IT operations Incident-triage delegating to SRE, security, and infra agents across clouds Cross-cloud without custom per-vendor SDKs The financial services momentum is the loudest signal. The Agent Payments Protocol (AP2) launched in September 2025 with Google and Coinbase, and by April 2026 had 60+ partner organizations including Adyen, American Express, Mastercard, MetaMask, PayPal, Revolut, Salesforce, and Worldpay. AP2 layers cryptographic payment mandates on top of A2A — the payment agents talk to each other over A2A, and the mandate is the payload. Alice Labs' Nordic client work in the same window has concentrated on IT operations (incident triage across Azure, AWS, and on-premise clouds for a Swedish energy client) and supply chain (multi-tier supplier delegation for a Nordic retail group). The engineering complexity has been much lower than the security and governance work — a recurring pattern in every A2A production project we have shipped. ### Want to discuss how this applies to your organization? Book a free 30-minute strategy call with our AI team. [Book a call](/en/ai-consulting-services#contact-form) 13 / 15 Chapter ## Agent Payments Protocol (AP2) and the x402 extension In short AP2 launched September 2025 with Google + Coinbase and 60+ partners; it uses cryptographic mandates so agents can autonomously execute payments with verifiable trust. The A2A x402 extension (co-developed with Coinbase and MetaMask) carries crypto payment authority alongside traditional card rails. The Agent Payments Protocol (AP2) is the fastest-moving A2A extension surface. It launched in September 2025 with Google and Coinbase leading, and by April 2026 had 60+ partners across issuers, networks, wallets, and platforms — Adyen, American Express, Mastercard, MetaMask, PayPal, Revolut, Salesforce, Worldpay, and more. The core primitive is a **cryptographic mandate**: a signed authorization an agent carries that lets it execute a payment within specific bounds (amount, payee, time window) without further human approval. Mandates travel as A2A message parts; payment agents on either side verify signatures before acting. The **A2A x402 extension**, co-developed with Coinbase and MetaMask, is the crypto rail. It carries payment authority for on-chain settlement alongside traditional card rails. Reference implementations live at [github.com/google-a2a/a2a-x402](https://github.com/google-a2a/a2a-x402) and are production-ready for crypto flows today. Card-based AP2 flows are further from production. Issuer support is still maturing, and the risk models for agent-initiated card payments are being negotiated inside each network. As of Q3 2026, x402 crypto flows are the fastest path to production for agentic commerce; card-based AP2 will follow as issuers publish their agent-payment policies. Alice Labs' guidance for enterprise clients evaluating AP2: treat it as a strategic surface to prototype on, but do not put mandated agent payments on the critical path of a live business process until your finance, legal, and compliance functions have signed off on the specific mandate scope. The technology is ahead of the governance in most large enterprises today. Start with a spending limit, not a scope The safest AP2 pilot design: cap the total authorized spend per agent per day at a low number (e.g. €500), independent of what the mandate scope allows. That way even a bad mandate cannot cause more than a bounded loss while your governance catches up. 14 / 15 Chapter ## When to use A2A vs in-process agent composition In short Use A2A when agents cross a service, team, cloud, or organizational boundary. Skip A2A when all agents live in the same process and are owned by the same team — agents-as-tools has less overhead. Alice Labs' rule: A2A between crews, agents-as-tools inside a crew. Not every multi-agent system needs A2A. The most common over-engineering mistake we see in Alice Labs' architecture reviews is teams putting HTTP hops between agents that live in the same Python process, owned by the same team, deployed to the same container. The decision framework we use: - **Use A2A** when agents cross a service boundary (different deployment units), a team boundary (different owners), a cloud boundary (Azure ↔ AWS ↔ GCP), or an organizational boundary (your company ↔ a partner). - **Skip A2A** when all agents live in the same process, are owned by the same team, and are deployed together. Use your framework's native agents-as-tools pattern instead — LangGraph sub-graphs, MAF handoffs, CrewAI role composition. The overhead A2A adds is real. Every A2A call is an HTTP round-trip — roughly 10–100ms of latency depending on network topology, plus serialization overhead on both ends. In-process function calls are microseconds. When a supervisor delegates to a specialist five times per user turn, the difference between in-process and A2A can be the difference between a snappy UX and a slow one. The other subtle cost: remote agents own their conversation state. A restarted remote agent can lose context that a client thought was preserved. Same-process agents share memory directly and do not have this failure mode. Alice Labs' rule of thumb, distilled from 100+ implementations: **A2A between crews, agents-as-tools inside a crew**. A crew is a bounded team of agents built and deployed together. Inside a crew, use your framework's native composition. Between crews — where a different team, different repo, or different cloud is involved — reach for A2A. For patterns on in-process multi-agent composition, see our [multi-agent systems guide](/en/insights/multi-agent-systems-explained) and our [AI agent orchestration deep-dive](/en/insights/ai-agent-orchestration). 15 / 15 Chapter ## Alice Labs A2A implementation playbook In short Five production practices from Alice Labs' 100+ implementations: start with a registry not point-to-point, enforce signed Agent Cards from day one, pair A2A with MCP inside every agent, instrument every Task state transition, and gate EU A2A endpoints with mTLS + OIDC for AI Act Article 12 compliance. Alice Labs has shipped A2A meshes into regulated Nordic and European enterprises across energy, financial services, retail, and public sector. Every project has surfaced variations of the same five lessons. 1. **Start with a registry, not point-to-point.** Even three agents benefit from a discovery layer — it removes hard-coded URLs, enables capability-based routing, and gives you a single place to enforce policy. Retrofitting a registry into a point-to-point mesh is painful; adding one on day one costs a day. 2. **Enforce signed Agent Cards from day one.** Unsigned cards are acceptable only in a single-team development environment. Production, staging, and anything a partner can call requires signed cards with a pinned trusted-issuer list. This is a two-line policy in most gateways. 3. **Pair A2A with MCP.** Give every A2A-exposed agent its own MCP-based tool belt. Callers should not need to know anything about internal tools, and internal tools should be swappable without breaking the external contract. This separation is what makes A2A endpoints stable over time. 4. **Instrument every Task state transition.** Log SUBMITTED, WORKING, INPUT\_REQUIRED, AUTH\_REQUIRED, COMPLETED, FAILED, CANCELED, and REJECTED to your observability stack. OpenTelemetry Agent semantic conventions are stabilizing in 2026 and are the recommended shape. Without this instrumentation, debugging a cross-cloud A2A mesh is guessing. 5. **For EU deployments, gate A2A endpoints with mTLS + OIDC.** Log Task inputs, outputs, and state transitions with sufficient granularity to satisfy EU AI Act Article 12 (record-keeping obligations for high-risk AI systems). This is the single most-asked question from Nordic legal teams during A2A rollouts. None of this is exotic. Every practice on this list is standard security and platform engineering applied to a new surface. The A2A protocol makes the surface possible; the playbook makes it safe to run. For the broader EU AI Act context these controls satisfy, see our [EU AI Act compliance checklist for 2026](/en/insights/eu-ai-act-compliance-checklist-2026). Alice Labs A2A engagements Alice Labs has delivered 100+ production AI implementations since 2023, including multi-cloud A2A orchestrations across Azure AI Foundry, AWS Bedrock AgentCore, and Google Vertex AI Agent Builder for clients in energy, financial services, retail, and public sector. ## About the Authors & Reviewers Published August 2, 2026 Written by ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Reviewed by August 2, 2026 ![Linus Ingemarsson - Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Published August 2, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is the A2A protocol in simple terms? ▾ The Agent-to-Agent (A2A) protocol is an open, HTTP-based standard that lets AI agents built on different frameworks, clouds, or vendors discover each other, authenticate, and delegate tasks. Alice Labs uses A2A whenever a LangGraph, Microsoft Agent Framework, or Google ADK agent needs to call another agent across a service or organizational boundary. It reached v1.0 in April 2026 and is governed by the Linux Foundation. ### Who created the A2A protocol? ▾ Google Cloud introduced A2A in April 2025 with 50+ launch partners including Salesforce, SAP, Accenture, and Deloitte. In June 2025, Google donated the protocol, specification, and SDKs to the Linux Foundation, which established the Agent2Agent project for vendor-neutral governance. Alice Labs treats the Linux Foundation spec at a2a-protocol.org as the canonical source of truth for client implementations. ### What is the current A2A spec version? ▾ A2A v1.0.0 is the current stable specification as of April 2026. It introduced signed Agent Cards, an extension mechanism, and multi-transport support (JSON-RPC 2.0, gRPC, and HTTP+JSON/REST). Previous releases (0.1.0, 0.2.6, 0.3.0) remain documented for migration purposes. Alice Labs recommends targeting v1.0 for all new production integrations and migrating older 0.x implementations by end of 2026. ### How does A2A compare to MCP? ▾ MCP (Model Context Protocol) standardizes how one agent talks to tools, data, and APIs. A2A standardizes how multiple agents talk to each other. They solve different layers and most Alice Labs production stacks use both: MCP inside each agent for tool access, A2A between agents for coordination. Reference pattern: an inventory agent uses MCP to check stock, then A2A to delegate a purchase order to a supplier's remote agent. ### Is A2A production-ready in 2026? ▾ Yes. A2A hit v1.0 stable in April 2026 with 150+ supporting organizations, 22,000+ GitHub stars, and production-ready SDKs in Python, JavaScript, Java, Go, and .NET. GA support ships inside Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock AgentCore, and Google Vertex AI. Alice Labs has shipped A2A meshes into regulated Nordic and European enterprises using signed Agent Cards and mTLS. ### What are Agent Cards in A2A? ▾ An Agent Card is a JSON document an agent publishes at a well-known path (typically /.well-known/agent-card.json). It declares identity, endpoint URL, skills, input/output modalities, and supported authentication schemes. Clients fetch the card, verify it, and know exactly how to call the agent. v1.0 added Signed Agent Cards using JSON Web Signature so callers can cryptographically verify the card was issued by the claimed agent. ### What transport protocols does A2A use? ▾ A2A v1.0 defines three transport bindings: JSON-RPC 2.0 over HTTP or WebSocket (reference), gRPC (for typed streaming), and HTTP+JSON/REST. All three carry the same Task, Message, and Artifact model. Server-Sent Events handles streaming Task updates; webhooks handle push notifications for long-running tasks. Alice Labs defaults to JSON-RPC over HTTPS with SSE for enterprise deployments because it maps cleanly to existing API gateways. ### Does Microsoft Agent Framework support A2A? ▾ Yes. Microsoft shipped A2A v1 support in the Agent Framework for .NET on April 28, 2026, with Python parity on July 30, 2026. Two package roles exist: A2A Agent (client-side) and A2A Hosting (server-side). Alice Labs uses MAF's AddA2AServer() plus MapA2AHttpJson() to expose Foundry agents as A2A endpoints, and A2ACardResolver on the client side to consume remote agents as standard AIAgent instances. ### Does LangGraph support A2A? ▾ Yes. LangSmith Agent Server exposes A2A endpoints at /a2a/{assistant\_id}, letting LangGraph orchestrators be called by any A2A-compliant client. On the client side, LangGraph orchestrators can query a registry, match capability tags on remote Agent Cards, and delegate subtasks via JSON-RPC 2.0. Native remote-A2A-agent-as-sub-graph is still an open community feature request. ### Does CrewAI support A2A? ▾ Yes. CrewAI ships A2A adapters so one Crew can be exposed as a standardized endpoint that another Crew or any A2A-compliant client can call. This lets a CrewAI-based specialist team be composed into a larger multi-framework mesh without leaking CrewAI-specific implementation details. ### Which authentication schemes does A2A support? ▾ A2A supports six authentication schemes: APIKey, HTTP Basic, HTTP Bearer, OAuth 2.0 (authorization code, client credentials, and device code flows), OpenID Connect, and mutual TLS. Schemes are advertised in the Agent Card but not enforced by the protocol — credential acquisition is delegated to implementers. Alice Labs' default for enterprise cross-boundary deployments is OpenID Connect plus mTLS. ### What are the main security risks of A2A? ▾ Named risks include agent impersonation, Agent Card tampering, replay attacks, webhook SSRF, and context poisoning of Agent Cards. The card-refresh timing gap can expose clients to compromised cards until they refetch. Mitigations: enforce signed Agent Cards, pin trusted issuers, validate JWS on every fetch, gate webhook targets by allowlist, and log every Task state transition to your SIEM. ### What is AP2 and how does it relate to A2A? ▾ The Agent Payments Protocol (AP2) launched in September 2025 with Google and Coinbase, and had 60+ payments and finserv partners by April 2026. It layers cryptographic payment mandates on top of A2A so agents can autonomously execute payments with verifiable trust. The x402 extension, co-developed with Coinbase and MetaMask, carries crypto payment authority alongside traditional card rails. ### Can I use A2A across AWS, Azure, and Google Cloud? ▾ Yes. GA A2A support ships inside Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock AgentCore Runtime, and Google Vertex AI Agent Builder as of April 2026. Cross-cloud A2A calls are plain HTTPS, so standard cloud networking (private endpoints, VPC peering, egress rules, API gateways) still applies. Alice Labs has shipped multi-cloud A2A orchestrations across all three major clouds. ### When should I use A2A vs in-process agent composition? ▾ Use A2A when agents cross a service, team, cloud, or organizational boundary. Skip A2A when all agents live in the same process and are owned by the same team — agents-as-tools has less overhead (microseconds vs 10–100ms of HTTP latency per call). Alice Labs' rule of thumb: A2A between crews, agents-as-tools inside a crew. ### How does A2A handle long-running tasks? ▾ A2A Tasks progress through explicit lifecycle states (SUBMITTED, WORKING, INPUT\_REQUIRED, AUTH\_REQUIRED, COMPLETED, FAILED, CANCELED, REJECTED). Server-Sent Events (SSE) push incremental updates over a single long-lived HTTP response. For Tasks that outlive a client connection, webhooks let the agent POST completion notifications to a caller-registered URL. Remote agents own their conversation state, keyed by context ID. ### Is A2A a competitor to OpenAI's function calling? ▾ No — different problem. OpenAI function calling is a model-level feature that lets a single model call tools during generation. A2A is a network protocol that lets agents call other agents across process, team, cloud, or organizational boundaries. Function calling maps to what MCP standardizes at the tool layer. A2A sits above that, at the agent-to-agent coordination layer. ### What SDKs are available for A2A in 2026? ▾ As of Q2 2026, five production-ready SDKs are available: Python, JavaScript, Java, Go, and .NET. That covers essentially every enterprise runtime an AI team is likely to standardize on. Because A2A is plain HTTP, any HTTP-capable runtime can implement it without an SDK — several teams have shipped A2A agents in Rust and Elixir with no framework at all. ### How does A2A support EU AI Act compliance? ▾ A2A's explicit Task lifecycle states and Agent Card discovery model make the record-keeping obligations under EU AI Act Article 12 tractable: log every state transition with caller identity, Agent Card issuer, and Task ID to your SIEM. Combined with mTLS + OpenID Connect on the ingress and signed Agent Cards on discovery, A2A meshes can satisfy the traceability and human-oversight requirements for high-risk AI systems. Always confirm scope with legal counsel. ### How do I get started building an A2A agent? ▾ For a MAF .NET agent: services.AddA2AServer(agent), then app.MapA2AHttpJson('/a2a') and app.MapWellKnownAgentCard(). For LangGraph: deploy to LangSmith Agent Server and use the auto-exposed /a2a/{assistant\_id} endpoint. For any HTTP runtime: publish a JSON Agent Card at /.well-known/agent-card.json and accept JSON-RPC 2.0 Task requests. Alice Labs recommends starting with signed Agent Cards from day one — it is trivial to add up front and painful to retrofit. [Previous in AI Agents ### LangGraph vs Claude Agent SDK 2026: Which to Choose? ](/en/insights/langgraph-vs-claude-agent-sdk-2026)[Next in AI Agents ### Model Context Protocol (MCP) Guide 2026: Complete Reference ](/en/insights/model-context-protocol-guide-2026) ## Further reading - [A2A Protocol v1.0 Specification — Linux Foundation](https://a2a-protocol.org/latest/specification/)· a2a-protocol.org - [A2A: A New Era of Agent Interoperability — Google Cloud](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)· developers.googleblog.com - [A2A Protocol Surpasses 150 Organizations — Linux Foundation press release](https://www.linuxfoundation.org/press/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year)· linuxfoundation.org - [A2A v1 in Microsoft Agent Framework for .NET — Microsoft devblog](https://devblogs.microsoft.com/agent-framework/a2a-v1-is-here-cross-platform-agent-communication-in-microsoft-agent-framework-for-net/)· devblogs.microsoft.com - [LangSmith Server A2A endpoint — LangChain docs](https://docs.langchain.com/langsmith/server-a2a)· docs.langchain.com - [Safeguarding AI Agents: A2A Protocol Risks — Palo Alto Networks](https://live.paloaltonetworks.com/t5/community-blogs/safeguarding-ai-agents-an-in-depth-look-at-a2a-protocol-risks/ba-p/1235996)· paloaltonetworks.com - [Agent Payments Protocol (AP2)](https://ap2-protocol.org/)· ap2-protocol.org ## Related services [AI agents development and deployment ](/en/ai-agents) ## Related reading [comparison ### Best AI Agent Frameworks 2026: The Enterprise Comparison Compare LangGraph, CrewAI, AutoGen, Microsoft Agent Framework, and Google ADK — every framework in this comparison now ships first-party A2A support. ](/en/insights/best-ai-agent-frameworks-2026)[howto ### LangGraph Tutorial 2026: Build Stateful AI Agents for Enterprise The framework that pairs most naturally with A2A on the orchestrator side — LangSmith Agent Server exposes A2A endpoints at /a2a/{assistant\_id} out of the box. ](/en/insights/langgraph-guide-2026)[deepdive ### CrewAI Guide 2026: Role-Based Multi-Agent Systems Role-based multi-agent patterns that compose cleanly with A2A between-crew delegation. ](/en/insights/crewai-guide-2026)[deepdive ### Multi-Agent Systems Explained: Patterns and Trade-offs The in-process alternatives to A2A — when to use supervisor, hierarchical, or collaborative patterns instead of adding HTTP hops. ](/en/insights/multi-agent-systems-explained)[deepdive ### AI Agent Orchestration: Patterns for Enterprise How orchestrators route work across agents — the layer that decides whether a given delegation stays in-process or crosses an A2A boundary. ](/en/insights/ai-agent-orchestration)[deepdive ### AI Agent Security Risks The broader security posture A2A meshes slot into — including the named threat models Palo Alto Networks and Tyk have published for agent networks. ](/en/insights/ai-agent-security-risks)[deepdive ### EU AI Act Compliance Checklist for 2026 The record-keeping and human-oversight obligations under EU AI Act Article 12 that A2A Task state logging is designed to satisfy. ](/en/insights/eu-ai-act-compliance-checklist-2026) ## Sources 1. [A2A: A New Era of Agent Interoperability](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)Google Cloud Developers Blog · Google Cloud “A2A protocol was introduced by Google Cloud in April 2025 with 50+ enterprise launch partners including Salesforce, SAP, Accenture, and Deloitte.” (accessed 2026-08-02) 2. [A2A Protocol Surpasses 150 Organizations, Lands in Major Cloud Platforms, and Sees Enterprise Production Use in First Year](https://www.linuxfoundation.org/press/a2a-protocol-surpasses-150-organizations-lands-in-major-cloud-platforms-and-sees-enterprise-production-use-in-first-year)Linux Foundation · Linux Foundation “By April 2026, A2A had 150+ supporting organizations, 22,000+ GitHub stars, and GA support inside Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock AgentCore, and Google Vertex AI Agent Builder.” (accessed 2026-08-02) 3. [A2A Protocol v1.0 Specification](https://a2a-protocol.org/latest/specification/)Agent2Agent Project · Linux Foundation “v1.0 defines five core objects (Task, Message, Part, Artifact, Agent Card), eight Task lifecycle states, three transport bindings (JSON-RPC 2.0, gRPC, HTTP+JSON/REST), and six authentication schemes.” (accessed 2026-08-02) 4. [A2A v1 is Here: Cross-Platform Agent Communication in Microsoft Agent Framework for .NET](https://devblogs.microsoft.com/agent-framework/a2a-v1-is-here-cross-platform-agent-communication-in-microsoft-agent-framework-for-net/)Microsoft Agent Framework team · Microsoft “Microsoft shipped A2A v1 support in MAF for .NET on April 28, 2026, with Python parity on July 30, 2026. Two package roles: A2A Agent (client-side) and A2A Hosting (server-side).” (accessed 2026-08-02) 5. [LangSmith Server A2A Endpoint](https://docs.langchain.com/langsmith/server-a2a)LangChain Documentation · LangChain Inc. “LangSmith Agent Server exposes A2A endpoints at /a2a/{assistant\_id} for every deployed LangGraph assistant.” (accessed 2026-08-02) 6. [MCP vs A2A: Understanding the Two Standards Powering Agentic AI](https://www.truefoundry.com/blog/mcp-vs-a2a)Truefoundry Engineering · Truefoundry “MCP hit 97M monthly SDK downloads by April 2026 (970x growth in 16 months); A2A hit 150+ supporting organizations and 22,000+ GitHub stars in the same window.” (accessed 2026-08-02) 7. [Safeguarding AI Agents: An In-Depth Look at A2A Protocol Risks](https://live.paloaltonetworks.com/t5/community-blogs/safeguarding-ai-agents-an-in-depth-look-at-a2a-protocol-risks/ba-p/1235996)Palo Alto Networks · Palo Alto Networks “Named A2A risks include agent impersonation, Agent Card tampering, replay attacks, webhook SSRF, and role fluidity between client and server that enables network-level impersonation.” (accessed 2026-08-02) 8. [Linux Foundation A2A Protocol Marks One Year with Broad Enterprise and Cloud Adoption](https://www.hpcwire.com/aiwire/2026/04/09/linux-foundation-a2a-protocol-marks-one-year-with-broad-enterprise-and-cloud-adoption/)HPCwire / AIwire · HPCwire “Active A2A production deployments as of April 2026 include supply chain, financial services, insurance, and IT operations verticals.” (accessed 2026-08-02) 9. [AP2 Protocol Overview](https://ap2-protocol.org/)Agent Payments Protocol · Google + Coinbase “AP2 launched September 2025 with 60+ payments and finserv partners including Adyen, American Express, Mastercard, MetaMask, PayPal, Revolut, Salesforce, and Worldpay. The x402 extension carries crypto payment authority alongside traditional card rails.” (accessed 2026-08-02) 10. [Agent-to-Agent in Microsoft Agent Framework](https://learn.microsoft.com/en-us/agent-framework/journey/agent-to-agent)Microsoft Learn · Microsoft “Use A2A when agents cross a service, team, cloud, or organizational boundary. Skip A2A when all agents live in the same process and are owned by the same team — agents-as-tools has less overhead.” (accessed 2026-08-02) 11. [Enterprise AI Implementation Data](https://alicelabs.ai/en/ai-agents)Alice Labs · Alice Labs “Alice Labs has delivered 100+ production AI implementations since 2023, including multi-cloud A2A orchestrations across Azure AI Foundry, AWS Bedrock AgentCore, and Google Vertex AI Agent Builder for clients in energy, financial services, retail, and public sector.” (accessed 2026-08-02) Next scheduled review: 2026-11-02 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. 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