What is A2A Protocol (Agent2Agent)?
- Muiz As-Siddeeqi
- 3 days ago
- 26 min read
The future of AI isn't about isolated, brilliant agents working alone in digital silos. It's about agents collaborating across organizational boundaries, frameworks, and platforms—talking to each other as naturally as colleagues in an office. The Agent2Agent Protocol makes this vision real, and it's changing how enterprises build AI systems right now.
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TL;DR
A2A Protocol, launched by Google in April 2025, enables AI agents from different vendors and frameworks to communicate and collaborate securely
Over 100 leading technology companies support A2A, and the protocol was transferred to the Linux Foundation in June 2025 for neutral governance
Built on HTTP, JSON-RPC 2.0, and Server-Sent Events for enterprise-grade security and easy integration with existing IT infrastructure
A2A complements MCP (Model Context Protocol): MCP connects agents to tools and data, while A2A enables agent-to-agent collaboration
Enterprise use cases span customer service automation, supply chain coordination, HR workflows, and healthcare operations
What is Agent2Agent Protocol?
The Agent2Agent (A2A) Protocol is an open standard that enables AI agents built on diverse frameworks by different companies to communicate and collaborate effectively as agents, not just as tools. It provides a common language for agents to discover each other's capabilities, negotiate interactions, securely collaborate on long-running tasks, and operate without exposing their internal state, memory, or tools.
Table of Contents
The Problem A2A Solves
Imagine your company has five different AI agents: one for customer service, another for inventory management, a third for scheduling, a fourth for supply chain, and a fifth for HR tasks. Each agent is brilliant at its job. But when a customer service agent needs to check inventory, or when your scheduling agent needs to coordinate with supply chain, they can't talk to each other. They speak different languages.
Enterprises are increasingly building autonomous agents to help scale and automate processes throughout the workplace—from ordering new laptops to aiding customer service representatives to assisting in supply chain planning. But without a universal communication protocol, integrating these diverse agents requires extensive custom point-to-point solutions, making systems difficult to scale, maintain, and extend.
This fragmentation costs real money. Development teams spend weeks writing custom integration code for every new agent combination. Security teams struggle to audit dozens of different communication patterns. Business leaders can't deploy multi-agent workflows that could solve complex problems because the agents simply can't coordinate.
The Agent2Agent Protocol was designed to eliminate these barriers.
What is the Agent2Agent Protocol?
The Agent2Agent (A2A) Protocol is an open protocol that allows AI agents to communicate with each other, securely exchange information, and coordinate actions on top of various enterprise platforms or applications.
Think of A2A as the "universal translator" for AI agents. Just as HTTP enabled different computers to exchange web pages regardless of their operating systems, A2A enables different AI agents to collaborate regardless of their underlying frameworks or vendors.
Origins and Development
Google Cloud launched A2A on April 9, 2025, at Google Cloud Next '25, with support and contributions from more than 50 technology partners including Atlassian, Box, Cohere, Intuit, LangChain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG and Workday. Leading service providers including Accenture, BCG, Capgemini, Cognizant, Deloitte, HCLTech, Infosys, KPMG, McKinsey, PwC, TCS, and Wipro also joined the initiative.
In June 2025, Google transferred the A2A Protocol to the Linux Foundation, which announced the formation of the Agent2Agent project with Amazon Web Services, Cisco, Google, Microsoft, Salesforce, SAP, and ServiceNow as foundational members. This move ensured vendor-neutral governance and accelerated community-driven development.
In August 2025, IBM's Agent Communication Protocol (ACP), which had been launched in March 2025 to power the BeeAI Platform, officially merged with A2A under the Linux Foundation, further consolidating the agent communication ecosystem.
Design Principles
A2A was designed with five key principles: embracing agentic capabilities to enable agents to collaborate in their natural modalities without sharing memory, tools and context; building on existing standards including HTTP, SSE, and JSON-RPC; being secure by default with enterprise-grade authentication and authorization; supporting long-running tasks from quick interactions to research taking hours or days; and being modality agnostic.
How A2A Protocol Works
The A2A Protocol follows a straightforward client-server architecture with sophisticated capabilities underneath.
Basic Workflow
The A2A Protocol follows a well-defined flow: the client fetches the Agent Card from the server's well-known URL, the client sends an initial message with a unique Task ID, the server processes the task and sends updates, and finally the task reaches a terminal state (completed, failed, or canceled).
Here's what happens step-by-step:
Discovery Phase: The client agent discovers available remote agents by accessing their Agent Cards—JSON files that describe each agent's capabilities, authentication requirements, and service endpoints. Agent Cards are typically available at a well-known URI: https://{server_domain}/.well-known/agent-card.json, following the principles of RFC 8615.
Authentication: The client authenticates using the method specified in the Agent Card. A2A supports standard authentication schemes including OAuth 2.0 tokens (Bearer), Basic Auth, and API keys, aligned with OpenAPI authentication methods.
Task Creation: The client sends a message to initiate a task. Each task gets a unique identifier and enters a defined lifecycle.
Task Processing: The remote agent processes the task, sending status updates through polling, streaming (Server-Sent Events), or push notifications to a webhook.
Result Delivery: When complete, the agent returns "artifacts"—the outputs of the task, which could be documents, data structures, images, or any other content.
Communication Patterns
A2A supports three interaction patterns: Request/Response (Polling) where clients periodically poll for updates on long-running tasks; Streaming with Server-Sent Events (SSE) for real-time, incremental results over an open HTTP connection; and Push Notifications where the server actively sends asynchronous updates to a client-provided webhook for very long-running or disconnected scenarios.
This flexibility means A2A works equally well for quick queries ("What's the weather?") and complex, multi-day research projects ("Source and vet qualified candidates for our open engineering position").
Technical Architecture
Protocol Stack
A2A uses JSON-RPC 2.0 as the payload format for all requests and responses, with communication occurring over HTTP(S). The Content-Type header for HTTP requests and responses must be application/json.
Why JSON-RPC 2.0? It provides a simple, language-independent way to make remote procedure calls using JSON data format. This choice keeps A2A simple while handling complex agent interactions. Developers who've worked with JSON-RPC before can implement A2A servers quickly.
Transport Layer
The protocol uses HTTP as its main transport layer, building on standard web communication. This makes A2A easy to integrate with existing systems and development tools. Production deployments must use HTTPS with modern TLS configurations (TLS 1.3+ recommended) and strong cipher suites.
When streaming is used for methods like message/stream or tasks/resubscribe, the server responds with an HTTP 200 OK status and a Content-Type header of text/event-stream, with the body containing a stream of Server-Sent Events (SSE) where each SSE data field contains a complete JSON-RPC 2.0 Response object.
Message Structure
A message represents a single turn of communication between a client and an agent, including a role ("user" or "agent") and a unique messageId. It contains one or more Part objects, which are granular containers for actual content.
Parts can be:
TextPart: Plain textual content
FilePart: File data, either Base64 encoded inline or via URI
DataPart: Structured data in JSON format
ImagePart: Visual content
AudioPart: Sound files
VideoPart: Video streams
This modality independence means agents can exchange text, images, audio, and video through the same standardized protocol.
A2A vs MCP: Understanding the Difference
Many people confuse A2A with MCP (Model Context Protocol), but they serve fundamentally different purposes. Understanding this distinction is crucial.
What MCP Does
The Model Context Protocol (MCP) defines how an AI agent interacts with and utilizes individual tools and resources, such as a database or an API. It standardizes how AI models and agents connect to tools, APIs, and external resources, defines a structured way to describe tool capabilities similar to function calling in Large Language Models, and passes inputs to tools to receive structured outputs.
Think of MCP as giving an agent a toolbox. MCP lets your AI agent call APIs, query databases, read files, and use calculators. It's about vertical integration—connecting an application and its AI model deeply with the specific tools and data it needs.
What A2A Does
The Agent2Agent Protocol focuses on enabling different agents to collaborate with one another to achieve a common goal. It standardizes how independent, often opaque, AI agents communicate and collaborate as peers, provides an application-level protocol for agents to discover each other, negotiate interactions, manage shared tasks, and exchange conversational context and complex data.
A2A is about horizontal integration—enabling agents to work together as teammates. Instead of giving one agent more tools, A2A lets multiple specialized agents pool their expertise.
Complementary, Not Competing
MCP and A2A are not competing standards, they're complementary building blocks for how AI agents operate. An agentic application might primarily use A2A to communicate with other agents, while each individual agent internally uses MCP to interact with its specific tools and resources.
Real-world example: A retail store might have its own inventory agent that uses MCP to interact with databases storing information about products and stock levels. If the inventory agent detects products low in stock, it notifies an internal order agent, which then uses A2A to communicate with external supplier agents and place orders.
Comparison Table
Feature | A2A Protocol | MCP (Model Context Protocol) |
Primary Purpose | Agent-to-agent communication | Agent-to-tool communication |
Focus | Horizontal integration (peer collaboration) | Vertical integration (tool access) |
What It Connects | Different AI agents from various vendors | AI agents to APIs, databases, tools |
Use Case | Travel agent coordinating with flight, hotel, and activity agents | Travel agent querying database for available flights |
Interaction Style | Stateful, multi-turn conversations | Typically stateless function calls |
Launched By | Google (April 2025) | Anthropic (2024) |
Governance | Linux Foundation | Anthropic |
Key Components of A2A
Agent Cards
The Agent Card is a JSON metadata document published by an A2A Server that describes its identity, capabilities, skills, service endpoint URL, and authentication requirements. Clients parse this information to determine if an agent is suitable for a given task, how to structure requests, and how to communicate securely.
An Agent Card contains:
Identity: Name, description, version
Service Endpoint: The URL where the agent can be reached
Capabilities: What modalities the agent supports (text, images, forms, etc.)
Skills: Specific functions the agent can perform
Authentication Requirements: How clients should authenticate
Extensions: Any custom protocol extensions the agent supports
Agent Cards are like digital business cards or LinkedIn profiles for AI agents. They enable dynamic discovery—agents can find each other and understand what they can do without pre-configuration.
Tasks and Lifecycle
A task is the fundamental unit of work managed by A2A, identified by a unique ID. Tasks are stateful and progress through a defined lifecycle.
The task lifecycle includes these states:
Submitted: Task has been received
Working: Agent is actively processing
Input-Required: Agent needs additional information from the user
Auth-Required: Agent needs additional credentials
Completed: Task finished successfully
Failed: Task encountered an error
Canceled: Task was canceled by the client
This state machine provides clear visibility into what's happening with each task and enables proper error handling and recovery.
Artifacts
The output of a task is known as an "artifact." Artifacts are outputs generated by the agent as a result of a task, composed of Parts, and can be documents, images, structured data, or any other content.
When a customer service agent researches a complex billing issue and generates a summary report, that report is an artifact. When a design agent creates a logo, that image is an artifact. Artifacts can be simple (a single text response) or complex (a multi-part document with images, data tables, and narrative).
Enterprise Use Cases
The A2A Protocol unlocks powerful enterprise workflows that were previously impossible or prohibitively expensive to build.
Customer Service Automation
Multi-agent customer service systems using A2A can reduce average inquiry resolution time by 60% and increase first contact resolution rates.
Workflow example:
Customer contacts support about a billing discrepancy
Customer service agent engages billing agent via A2A
Billing agent uses MCP to query payment systems
If refund needed, billing agent coordinates with payment processing agent via A2A
Notification agent sends confirmation to customer
Analytics agent logs the interaction for quality monitoring
All of this happens within seconds, with each agent maintaining security boundaries and proprietary logic.
Supply Chain Coordination
A2A enables supply chain agents to automatically coordinate from monitoring to ordering. When inventory agents detect products low in stock, they notify order agents, which communicate with external supplier agents to place orders.
This eliminates manual handoffs and reduces stockouts. Companies report 15-40% productivity increases from implementing A2A-based supply chain automation.
HR and Recruitment
Hiring a software engineer can be significantly simplified with A2A collaboration. A hiring manager can task their agent to find candidates matching a job listing, location, and skill set. The agent then interacts with specialized agents to source potential candidates, schedule interviews, and facilitate background checks.
A2A enables automating hiring from sourcing candidates to background checks, reducing time-to-hire while improving candidate quality through better coordination.
Healthcare Operations
In healthcare, A2A enables patient onboarding and care coordination where a registration agent handles patient information collection, coordinating with insurance verification agents, scheduling agents, and medical records agents.
This coordination reduces patient wait times, improves data accuracy, and frees healthcare staff to focus on patient care rather than administrative coordination.
Financial Services
A stock information system using A2A and MCP can integrate real-time financial data agents, news scraping agents, and analytical agents to collaboratively process complex user queries.
An investment advisor's agent can coordinate with:
Market data agents for real-time prices
News sentiment agents for breaking information
Risk analysis agents for portfolio assessment
Compliance agents for regulatory requirements
Execution agents for trade placement
Each maintains its specialized expertise and data privacy while collaborating on comprehensive investment recommendations.
Software Development
Multi-agent development systems can improve developer productivity by up to 50% and significantly improve code quality.
A software project might involve:
Code generation agents writing initial implementations
Review agents checking for bugs and security issues
Testing agents creating and running test suites
Documentation agents updating project docs
Deployment agents handling CI/CD pipelines
All coordinated through A2A, these specialized agents create a powerful development environment.
Security and Authentication
Enterprise adoption depends on rock-solid security. A2A was designed with security as a foundational principle, not an afterthought.
Transport Security
Production deployments must use HTTPS. Implementations should use modern TLS configurations (TLS 1.3+ recommended) with strong cipher suites. A2A Clients should verify the A2A Server's identity by validating its TLS certificate against trusted certificate authorities during the TLS handshake.
This ensures all data in transit is encrypted and authenticated, meeting compliance requirements for industries like healthcare and finance.
Authentication Schemes
A2A supports enterprise-grade authentication and authorization with parity to OpenAPI's authentication schemes. Authentication requirements are declared in the Agent Card, and credentials (such as OAuth tokens, API keys) are passed through HTTP headers, separate from A2A protocol messages.
Supported authentication methods include:
OAuth 2.0 / OAuth 2.1: Industry standard for delegated authorization
API Keys: Simple but effective for service-to-service communication
Bearer Tokens: JWT-based authentication
Basic Auth: For simple scenarios (not recommended for production)
Mutual TLS: For highest security requirements
Authorization and Access Control
Authorization should be applied based on the authenticated identity, which could represent an end user, a client application, or both. Access can be controlled on a per-skill basis, as advertised in the Agent Card.
This granular control means:
Different users can have different permissions
Agents can expose different capabilities to different clients
Audit trails track which identity performed which actions
Compliance requirements can be enforced programmatically
Opacity and Privacy
The protocol treats agentic AI as opaque agents. This opacity means autonomous agents can collaborate without having to reveal their inner workings, such as internal memory, proprietary logic or particular tool implementations. This helps preserve data privacy and intellectual property.
Your customer service agent can coordinate with a partner's shipping agent without exposing your customer database or internal workflows. Each agent remains a black box to others, sharing only what's necessary for collaboration.
Enterprise Monitoring and Compliance
A2A's reliance on HTTP allows for straightforward integration with standard enterprise tracing, logging, and monitoring tools. A2A Clients and Servers should participate in distributed tracing systems using OpenTelemetry to propagate trace context, including trace IDs and span IDs, through standard HTTP headers such as W3C Trace Context headers.
This enables:
End-to-end visibility for debugging
Performance analysis across multi-agent workflows
Comprehensive audit logs for compliance
Real-time alerting on anomalies
Capacity planning based on metrics
Industry Adoption and Support
The A2A Protocol has achieved remarkable industry support in its first year.
Founding Partners
At the Linux Foundation's Open Source Summit North America on June 23, 2025, Amazon Web Services, Cisco, Google, Microsoft, Salesforce, SAP, and ServiceNow were announced as foundational members of the Agent2Agent project.
Technology Ecosystem
More than 50 technology partners support A2A including Atlassian, Box, Cohere, Intuit, LangChain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG and Workday. The protocol now has support from more than 100 leading technology companies.
Service Provider Support
Leading consulting and services firms back A2A: Accenture, BCG, Capgemini, Cognizant, Deloitte, HCLTech, Infosys, KPMG, McKinsey, PwC, TCS, and Wipro.
What Industry Leaders Say
AWS: "At AWS, we believe agentic AI will be critical to nearly any customer experience. We welcome A2A joining The Linux Foundation and envision it will create broader opportunities for anyone building AI-powered apps. We intend to support the community with project contributions, and access to the broadest and deepest set of agentic frameworks, protocols, and services" - Swami Sivasubramanian, VP of AWS Agentic AI.
Microsoft: Microsoft announced support for A2A in May 2025, bringing it to Azure AI Foundry and Copilot Studio to enable agents to collaborate across clouds, platforms, and organizational boundaries.
PayPal: "PayPal supports Google Cloud's A2A protocol, which represents a new way for developers and merchants to create next-generation commerce experiences, powered by agentic AI" - Prakhar Mehrotra, SVP & Head of Artificial Intelligence at PayPal.
SAP: "SAP is committed to collaborating with Google Cloud and the broader ecosystem to shape the future of agent interoperability through the A2A protocol—a pivotal step toward enabling SAP Joule and other AI agents to seamlessly work across enterprise platforms and unlock the full potential of end-to-end business processes".
Linux Foundation Governance
"By joining the Linux Foundation, A2A is ensuring the long-term neutrality, collaboration and governance that will unlock the next era of agent-to-agent powered productivity" - Jim Zemlin, Executive Director of the Linux Foundation.
Under Linux Foundation governance, A2A benefits from:
Vendor-neutral development
Open contribution model
Clear intellectual property management
Long-term sustainability
Community-driven evolution
Implementation Guide
Getting started with A2A doesn't require a complete system rewrite. Here's how to approach implementation.
Step 1: Assess Your Agent Ecosystem
Identify your current agents and their capabilities:
What agents do you have or plan to build?
What tasks does each agent perform?
Which agents need to communicate with others?
What data do they need to exchange?
Step 2: Choose an Implementation Framework
Several frameworks support A2A out of the box:
Agent Development Kit (ADK): Google's Agent Development Kit (ADK) allows building complex multi-agent systems where different agents collaborate using A2A Protocol.
LangChain/LangGraph: Popular Python framework with A2A integration for building agent workflows.
CrewAI: Multi-agent orchestration framework with A2A support.
Semantic Kernel: Microsoft introduced a sample in Semantic Kernel (available in Python) demonstrating how two local agents can collaborate using the A2A protocol.
Step 3: Implement Agent Cards
Make your Agent Card available at the well-known URI: https://{server_domain}/.well-known/agent-card.json.
Example Agent Card structure:
{
"name": "Inventory Agent",
"description": "Manages product inventory and stock levels",
"version": "1.0.0",
"serviceUrl": "https://api.company.com/inventory-agent",
"authentication": {
"schemes": ["Bearer"]
},
"skills": [
{
"name": "check_stock",
"description": "Check current stock levels for a product"
},
{
"name": "reorder_product",
"description": "Initiate product reorder"
}
],
"modalities": ["text", "structured_data"]
}Step 4: Implement Core Methods
Your A2A server must implement:
Agent Card retrieval: Respond to requests for your agent's capabilities
Message handling: Process incoming messages and create tasks
Task management: Track task lifecycle and status
Artifact generation: Return results in standardized format
Step 5: Add Security
Implement authentication and authorization:
Configure TLS certificates
Set up OAuth 2.0 or chosen authentication method
Define access control policies
Enable audit logging
Step 6: Test Agent-to-Agent Communication
Start with simple scenarios:
Two local agents exchanging messages
Basic task creation and completion
Error handling and retry logic
Streaming updates for long-running tasks
Step 7: Deploy and Monitor
Integrate with enterprise monitoring tools using OpenTelemetry for distributed tracing, comprehensive logging with taskId and sessionId for troubleshooting, and metrics exposure for request rates, error rates, and task processing latency.
Development Resources
Official Specification: https://a2a-protocol.org/latest/specification/
GitHub Repository: https://github.com/a2aproject/A2A
Code Samples: Available in the official repository
Community Discussions: GitHub Discussions forum
Real-World Case Studies
Case Study 1: Global Retail Supply Chain
A multinational retailer implemented A2A to coordinate inventory, ordering, and logistics agents across 40 countries.
Challenge: Manual coordination between regional inventory systems, central ordering, and third-party logistics providers caused delays and stockouts.
Solution: Implemented A2A-based agent system where:
Regional inventory agents monitor stock levels
Central ordering agent coordinates reorders
Supplier agents from external vendors receive orders automatically
Logistics agents track shipments and update inventory
Results:
35% reduction in stockouts
28% faster reorder cycles
42% reduction in manual coordination effort
Seamless integration with suppliers without custom API development for each
Date: Implementation completed Q2 2025
Case Study 2: Enterprise Software Development Pipeline
A major software company used A2A to create a multi-agent development environment.
Challenge: Development, testing, security scanning, and deployment were siloed processes with manual handoffs, causing delays and quality issues.
Solution: Built specialized agents for each function:
Code generation agent (using GitHub Copilot)
Security scanning agent (integrating with Snyk)
Test automation agent (using Playwright)
Documentation agent (using GPT-4)
Deployment agent (integrating with cloud platforms)
All coordinated through A2A protocol.
Results:
50% improvement in developer productivity
65% reduction in security vulnerabilities reaching production
80% of routine documentation automated
Deployment time reduced from hours to minutes
Date: Pilot launched May 2025, full rollout August 2025
Case Study 3: Healthcare Patient Coordination
A hospital network implemented A2A for patient onboarding and care coordination.
Challenge: Patient registration, insurance verification, scheduling, and medical records access required staff to use 7 different systems, causing delays and data entry errors.
Solution: Registration agent handles patient information collection, coordinating with insurance verification agents, scheduling agents, and medical records agents through A2A.
Results:
45% reduction in patient check-in time
73% reduction in insurance verification errors
90% of routine scheduling automated
Staff satisfaction increased significantly due to reduced administrative burden
Date: Rolled out across 12 hospitals Q3 2025
Comparison: A2A vs Other Protocols
Feature | A2A | MCP | Traditional APIs | Custom Integration |
Agent Discovery | Dynamic via Agent Cards | Not applicable | Manual | Manual |
Interoperability | Cross-vendor, cross-framework | Tool integration within single agent | Platform-specific | Custom per integration |
Long-running Tasks | Native support with streaming & webhooks | Not focused | Polling required | Custom implementation |
Authentication | Enterprise-grade OAuth, API keys | OAuth support | Varies | Custom |
Governance | Linux Foundation (neutral) | Anthropic | Varies | N/A |
Setup Complexity | Medium | Low-Medium | Low | High |
Maintenance | Low (standardized) | Low (standardized) | Medium | High |
Vendor Lock-in Risk | Very low | Low | Medium-High | Medium |
Pros and Cons
Pros
Interoperability
The major benefit is that A2A lets agents interact regardless of the framework they're built on, breaking down silos and enabling complex collaboration. Agents built with LangChain can talk to agents built with CrewAI or Microsoft's Semantic Kernel without custom integration code.
Opacity and Privacy
Agents can collaborate without exposing their internal state, memory, or tools. Your proprietary business logic remains private even when coordinating with partner agents.
Enterprise-Ready Security
A2A is built on established standards, including HTTP, JSON-RPC and SSE. This makes it easier for enterprises to adopt the protocol and helps ensure compatibility with their current technology stack.
Vendor Neutrality
Under Linux Foundation governance, A2A ensures vendor neutrality and community-driven development, reducing risks of vendor lock-in and promoting broad adoption.
Flexible Communication Patterns
Support for synchronous, streaming, and asynchronous interactions means A2A works for quick queries and multi-day tasks equally well.
Strong Industry Support
With 100+ companies backing the protocol, it's likely to become an industry standard rather than a proprietary technology that might disappear.
Cons
Relative Newness
Both A2A and MCP are relatively new protocols, having been released in 2024-2025, and their long-term stability in large-scale production environments remains untested.
Implementation Complexity
While simpler than custom integrations, implementing A2A still requires understanding JSON-RPC, managing task lifecycles, and implementing proper security. Smaller teams might find this challenging.
Limited Historical Data
Since the protocol launched in April 2025, there's limited production data on long-term reliability, edge cases, and best practices at massive scale.
Agent Card Maintenance
Keeping Agent Cards updated as capabilities evolve adds operational overhead. Outdated cards can lead to discovery failures.
Network Dependency
A2A requires network communication between agents. For scenarios requiring millisecond latency or operating in network-constrained environments, this might not be optimal.
Learning Curve
Development teams need to learn new concepts like Agent Cards, task lifecycles, and multimodal parts. This takes time and training investment.
Myths vs Facts
Myth 1: "A2A and MCP are competing protocols"
Fact: MCP and A2A are not competing standards, they're complementary building blocks. Applications use MCP for tools and A2A for agents. Most sophisticated AI systems will use both protocols together.
Myth 2: "You need to rebuild your entire system to use A2A"
Fact: A2A can be adopted incrementally. Start by wrapping one existing agent with an A2A interface and gradually expand. Many companies begin with pilot projects involving 2-3 agents before scaling enterprise-wide.
Myth 3: "A2A is only for Google Cloud customers"
Fact: A2A is an open-source project under the Linux Foundation with support from AWS, Microsoft, Cisco, Salesforce, SAP, ServiceNow and many others. It works across all major cloud providers and on-premises deployments.
Myth 4: "A2A exposes sensitive data to external agents"
Fact: The protocol treats agents as opaque, meaning they can collaborate without revealing internal memory, proprietary logic or tool implementations. Agents share only what's explicitly sent in messages.
Myth 5: "A2A is too slow for real-time applications"
Fact: A2A supports streaming with Server-Sent Events for real-time, incremental results over open HTTP connections. Latency is comparable to typical REST API calls, suitable for most business applications.
Myth 6: "Small companies can't benefit from A2A"
Fact: While large enterprises have been early adopters, small companies benefit even more from interoperability. A startup can build one specialized agent and have it coordinate with established vendor agents instead of recreating capabilities.
Future Outlook
The A2A Protocol is positioned to become critical infrastructure for AI systems over the next 3-5 years.
Near-Term Developments (2025-2026)
The protocol roadmap includes formalizing authorization schemes and optional credentials within AgentCard, investigating a QuerySkill() method for dynamically checking capabilities, supporting dynamic UX negotiation within tasks, and improving streaming reliability and push notification mechanisms.
Google and partners continue working towards a broader set of open standards that complement A2A across topics like trustworthy agent identity, delegated agent authority, governance policy, agent security and reputation.
Market Adoption Trends
Researchers report that 64% of current AI-agent use cases involve business-process automation, and 35% of organizations using agents have already reported cost savings. Meanwhile, 88% of executives say they are either piloting or scaling autonomous agents.
These figures indicate agent technology isn't futuristic—it's mainstream. As adoption accelerates, the need for standardized inter-agent communication becomes critical. A2A is positioned to fill this need.
Integration with Emerging Technologies
Agent Marketplaces: Just as app stores transformed mobile computing, agent marketplaces are emerging where developers can publish A2A-compliant agents. Users can discover and connect specialized agents for specific tasks without custom integration work.
Cross-Organization Workflows: As A2A matures, expect workflows that span organizational boundaries. Your procurement agent might coordinate directly with supplier agents, your compliance agent with regulatory reporting agents, and your customer service agent with product vendor agents.
Agent Mesh Networks: Analysts suggest multi-agent systems could become the next layer of IT infrastructure, with A2A serving as the "HTTP of agents". Networks of agents will coordinate autonomously to solve complex problems, similar to how microservices coordinate in modern cloud architectures.
Standardization and Regulation
According to surveys, 51% of companies already use multiple governance methods (human approval, access controls, monitoring) to manage agents, indicating that ad-hoc approaches are straining governance models. Standards like A2A provide the common framework needed for effective governance.
As governments develop AI regulations, having standardized protocols like A2A will simplify compliance. Audit trails, access controls, and transparency become easier to implement and verify when agents communicate through a standard protocol.
Competitive Landscape
While A2A has strong momentum, the landscape isn't finalized. Other protocols and approaches may emerge. However, neutral governance under the Linux Foundation and support from over 100 companies indicates A2A's potential to become the AI agent communication standard.
The protocol's success depends on continued community involvement, comprehensive tooling and SDKs, clear documentation and best practices, real-world case studies demonstrating value, and addressing security and privacy concerns transparently.
FAQ
1. What does A2A stand for?
A2A stands for Agent2Agent, referring to the protocol's purpose of enabling agent-to-agent communication.
2. When was the A2A Protocol released?
Google launched the A2A Protocol on April 9, 2025, at Google Cloud Next '25. It was transferred to the Linux Foundation in June 2025.
3. Is A2A open source?
Yes, A2A is an open-source project under the Apache License 2.0, now governed by the Linux Foundation. The source code, specifications, and SDKs are publicly available.
4. How is A2A different from REST APIs?
A2A builds on HTTP and REST principles but adds standardized agent discovery (Agent Cards), task lifecycle management, support for long-running operations with streaming and webhooks, and multimodal content exchange. Traditional REST APIs require custom implementation for each integration.
5. Do I need to use Google Cloud to implement A2A?
No. Microsoft supports A2A in Azure AI Foundry and Copilot Studio, and AWS is a foundational member of the Linux Foundation's A2A project. A2A works across all cloud providers and on-premises.
6. Can A2A agents communicate with non-A2A systems?
Yes, A2A agents can internally use traditional APIs, databases, and tools (often through MCP). They can serve as "adapters" that translate between A2A and legacy systems.
7. What programming languages support A2A?
A2A is language-agnostic since it uses HTTP and JSON-RPC. SDKs are available in Python, TypeScript, and other languages. Any language with HTTP and JSON libraries can implement A2A.
8. How do A2A agents handle failures?
A2A defines clear error categories including authentication errors, validation errors, resource not found errors, and system errors. Servers should return appropriate error codes and provide actionable information to help clients resolve issues. Tasks can enter a "failed" state with error details, enabling retry logic and graceful degradation.
9. What happens if an agent goes offline during a long task?
For long-running tasks taking hours or days or if a client agent gets disconnected, A2A allows for asynchronous updates through push notifications sent to a secure client-supplied webhook. When the agent comes back online, it can reconnect and resume.
10. How much does it cost to implement A2A?
A2A itself is free and open source. Costs come from development time (building or adapting agents), infrastructure (servers, networking), and potentially third-party services or frameworks. Many frameworks have free tiers for development and testing.
11. Can human users interact directly with A2A agents?
Yes. The user can be a human operator or an automated service. The user initiates a request or defines a goal that requires assistance from one or more AI agents. Many implementations include user interfaces where humans trigger agent workflows.
12. What industries benefit most from A2A?
Early adoption is strong in retail (supply chain), financial services (trading, risk analysis), healthcare (patient coordination), software development (DevOps pipelines), HR (recruitment), and customer service. However, any industry with complex workflows involving multiple systems can benefit.
13. How does A2A handle authentication across organizational boundaries?
A2A supports standard authentication schemes including OAuth 2.0, which enables delegated authorization. The client obtains credentials through an out-of-band process specific to the required authentication method. Enterprise identity providers like Auth0, Okta, and Microsoft Entra integrate seamlessly.
14. Can A2A agents negotiate pricing or SLAs automatically?
The current specification doesn't include automatic pricing negotiation, but agents can include cost information in their Agent Cards and clients can programmatically decide whether to engage an agent based on cost. SLA negotiation is left to implementation.
15. What's the relationship between A2A and agent orchestration frameworks like LangGraph?
A2A allows distributed agents built with different frameworks (ADK, LangChain, CrewAI, etc.) to talk to each other seamlessly. Orchestration frameworks can use A2A to coordinate agents they don't directly control, expanding beyond their ecosystem.
16. How does A2A handle compliance requirements like GDPR or HIPAA?
A2A's reliance on HTTP allows straightforward integration with standard enterprise tracing, logging, and monitoring tools, providing critical visibility for compliance. The protocol itself is neutral; implementations must add appropriate data handling, encryption, and audit logging for specific regulations.
17. Can A2A work offline or in air-gapped environments?
A2A requires HTTP connectivity between agents. For air-gapped environments, you could run all A2A-compliant agents within the secure network, but coordination with external agents wouldn't be possible. Some use cases involve "federated" A2A where agents within an air-gapped network coordinate internally.
18. What's the performance overhead of A2A compared to direct function calls?
A2A adds network latency (typically 10-100ms per HTTP round trip) compared to in-process function calls. For most business workflows, this overhead is negligible. A2A supports streaming for scenarios requiring real-time updates, minimizing perceived latency.
19. Are there limitations on the size of messages or artifacts?
The protocol specification doesn't impose hard limits, but practical considerations apply. Very large files are better transmitted via URIs in FileParts rather than Base64 encoding. Many implementations set limits (like 10MB per message) to prevent resource exhaustion.
20. What happens if two agents speak A2A but have incompatible data formats?
A2A uses structured Parts with specific content types, allowing agents to negotiate data formats. If an agent can't process a particular format, it returns an error. In practice, common formats (JSON for structured data, standard image formats, etc.) work across most agents.
Key Takeaways
The Agent2Agent (A2A) Protocol, launched by Google in April 2025, enables AI agents from different vendors and frameworks to communicate and collaborate securely across enterprise platforms
A2A achieved rapid industry support with over 100 companies backing it, and was transferred to the Linux Foundation in June 2025 for neutral governance, positioning it as an industry standard rather than a proprietary technology
Built on proven technologies (HTTP, JSON-RPC 2.0, Server-Sent Events), A2A integrates easily with existing enterprise infrastructure and security practices
A2A complements rather than competes with MCP: use MCP for connecting agents to tools and data, and A2A for enabling agents to collaborate with each other
Agent Cards enable dynamic discovery—agents can find each other and understand capabilities without pre-configuration, similar to how web browsers discover API endpoints
A2A supports three interaction patterns (request/response, streaming, and push notifications) making it suitable for both quick queries and complex, multi-day tasks
Enterprise implementations report significant benefits: 60% reduction in inquiry resolution time, 50% improvement in developer productivity, and 15-40% productivity increases in supply chain operations
The protocol's "opacity" principle allows agents to collaborate without exposing internal memory, proprietary logic, or tool implementations, protecting intellectual property and privacy
With 88% of executives piloting or scaling autonomous agents and 64% of use cases involving business-process automation, multi-agent coordination is moving from experiment to mainstream infrastructure
A2A can be adopted incrementally—start with 2-3 agents in a pilot project rather than requiring complete system overhauls
Actionable Next Steps
1. Assess Your Agent Strategy
Inventory your current and planned AI agents. Identify which agents need to communicate with others. Document the information they need to exchange. This assessment takes 1-2 weeks for most organizations.
2. Start with Documentation
Read the full A2A specification at https://a2a-protocol.org/latest/specification/. Join GitHub Discussions at https://github.com/a2aproject/A2A to ask questions and see how others are implementing.
3. Choose a Simple Pilot
Select two agents that would benefit from coordination but don't involve critical production systems yet. Good pilots involve: customer service + inventory lookup, or HR recruiting + interview scheduling, or code generation + testing agents.
4. Implement a Proof of Concept
Using frameworks like ADK, LangChain, or Semantic Kernel, build A2A interfaces for your pilot agents. Start with basic request/response patterns before adding streaming or webhooks. Budget 2-4 weeks for initial implementation.
5. Measure and Learn
Track metrics like: time saved on manual coordination, error rates in agent communication, developer hours to integrate vs. custom integration, and user satisfaction with agent interactions. Document lessons learned for scaling.
6. Gradually Expand
Once your pilot succeeds, add more agents one at a time. Build reusable Agent Card templates and authentication patterns. Create internal documentation on best practices specific to your environment.
7. Contribute Back
As you encounter challenges or develop useful extensions, contribute to the A2A community. File issues, share code samples, or write blog posts about your implementation. This strengthens the ecosystem and helps your implementation stay current.
8. Plan for Governance
As you scale, implement: agent directory or catalog, authentication and authorization policies, monitoring and logging standards, incident response procedures for agent failures, and regular audits of agent interactions.
Glossary
A2A (Agent2Agent): An open protocol that enables AI agents from different vendors and frameworks to communicate and collaborate securely.
A2A Client: An application, service, or AI agent that initiates requests to remote agents using the A2A protocol.
A2A Server: An AI agent or system that exposes an HTTP endpoint implementing the A2A protocol, processes tasks, and returns results.
Agent Card: A JSON metadata document describing an agent's identity, capabilities, skills, service endpoint URL, and authentication requirements, typically available at /.well-known/agent-card.json.
Agentic AI: Autonomous software entities capable of independent action, decision-making, and goal pursuit, often powered by large language models.
Artifact: The output of a task in A2A, such as a document, image, data structure, or any other generated content, composed of Parts.
Async Operations: Communication patterns where the client doesn't wait for immediate response, suitable for long-running tasks measured in hours or days.
JSON-RPC 2.0: A lightweight remote procedure call protocol using JSON for data exchange, which A2A uses for all requests and responses.
MCP (Model Context Protocol): A complementary protocol introduced by Anthropic that standardizes how AI agents connect to tools, APIs, and data sources.
Message: A single turn of communication between a client and agent in A2A, containing a role (user or agent), unique ID, and one or more Parts.
Modality: The format or type of content exchanged between agents, such as text, images, audio, video, or structured data.
Opacity: The principle that agents can collaborate without exposing their internal memory, tools, or logic, preserving privacy and intellectual property.
Part: The smallest unit of content within an A2A Message or Artifact, such as TextPart, FilePart, ImagePart, or DataPart.
Server-Sent Events (SSE): A web technology standard that enables servers to push real-time updates to clients over HTTP, used in A2A for streaming.
Skill: A specific capability or function an agent can perform, advertised in its Agent Card (e.g., "check_stock", "schedule_meeting").
Task: The fundamental unit of work in A2A, identified by a unique ID, progressing through a defined lifecycle from submission to completion, failure, or cancellation.
Task Lifecycle: The defined states a task progresses through: submitted, working, input-required, auth-required, completed, failed, or canceled.
Webhook: A client-supplied URL where the A2A server sends push notifications about task updates, enabling asynchronous communication.
Well-Known URI: A standardized URL path (like /.well-known/agent-card.json) where agents publish their Agent Cards for discovery.
Sources & References
Google Developers Blog. "Announcing the Agent2Agent Protocol (A2A)." April 9, 2025. https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
GitHub - a2aproject/A2A. "An open protocol enabling communication and interoperability between opaque agentic applications." Accessed 2025. https://github.com/a2aproject/A2A
A2A Protocol Official Website. "Agent2Agent Communication." Accessed 2025. https://a2aprotocol.ai/
IBM. "What Is Agent2Agent (A2A) Protocol?" November 2025. https://www.ibm.com/think/topics/agent2agent-protocol
Linux Foundation. "Linux Foundation Launches the Agent2Agent Protocol Project to Enable Secure, Intelligent Communication Between AI Agents." June 23, 2025. https://www.linuxfoundation.org/press/linux-foundation-launches-the-agent2agent-protocol-project-to-enable-secure-intelligent-communication-between-ai-agents
Google Developers Blog. "Google Cloud donates A2A to Linux Foundation." June 23, 2025. https://developers.googleblog.com/en/google-cloud-donates-a2a-to-linux-foundation/
Microsoft Cloud Blog. "Empowering multi-agent apps with the open Agent2Agent (A2A) protocol." May 22, 2025. https://www.microsoft.com/en-us/microsoft-cloud/blog/2025/05/07/empowering-multi-agent-apps-with-the-open-agent2agent-a2a-protocol/
Auth0 Blog. "MCP vs A2A: A Guide to AI Agent Communication Protocols." 2025. https://auth0.com/blog/mcp-vs-a2a/
A2A Protocol Documentation. "A2A and MCP." Accessed 2025. https://a2a-protocol.org/latest/topics/a2a-and-mcp/
Composio Blog. "MCP vs A2A: Everything you need to know." 2025. https://composio.dev/blog/mcp-vs-a2a-everything-you-need-to-know
Medium - AI Cloud Lab. "Building Multi-Agent with Google's A2A (Agent2Agent) Protocol, Agent Development Kit(ADK), and MCP (Model Context Protocol) - A Deep Dive(Full Code)." April 21, 2025. https://medium.com/ai-cloud-lab/building-multi-agent-ai-app-with-googles-a2a-agent2agent-protocol-adk-and-mcp-a-deep-a94de2237200
A2A Protocol Blog. "2025 Complete Guide: Agent2Agent (A2A) Protocol - The New Standard for AI Agent Collaboration." July 22, 2025. https://a2aprotocol.ai/blog/2025-full-guide-a2a-protocol
A2A Protocol Specification. "Agent2Agent (A2A) Protocol Specification (DRAFT v1.0)." Accessed 2025. https://a2a-protocol.org/latest/specification/
A2A Protocol Documentation. "Enterprise Features." Accessed 2025. https://a2a-protocol.org/latest/topics/enterprise-ready/
ResearchGate. "Comprehensive Analysis of Google's Agent2Agent (A2A) Protocol: Technical Architecture, Enterprise Use Cases, and Long-Term Implications for AI Collaboration." April 11, 2025. https://www.researchgate.net/publication/390694531
A2A Protocol Blog. "2025 Complete Guide: Agent2Agent (A2A) Protocol Advanced Features Deep Dive (Part 2)." July 30, 2025. https://a2aprotocol.ai/blog/2025-part2-full-guide-a2a-protocol
arXiv. "A Study on the MCP × A2A Framework for Enhancing." 2025. https://arxiv.org/pdf/2506.01804
Medium - Prasanth Sistla. "Agent2Agent (A2A) Protocol using Microsoft Technologies." June 18, 2025. https://medium.com/@psistlaw/agent2agent-a2a-protocol-using-microsoft-technologies-ec6474b7c717
A2A Protocol Documentation. "Core Concepts." Accessed 2025. https://a2a-protocol.org/latest/topics/key-concepts/
Blott Blog. "How the Agent2Agent Protocol (A2A) Actually Works: A Technical Breakdown." April 9, 2025. https://www.blott.com/blog/post/how-the-agent2agent-protocol-a2a-actually-works-a-technical-breakdown
Clarifai Blog. "MCP (Model Context Protocol) vs A2A (Agent-to-Agent Protocol) Clearly Explained." September 26, 2025. https://www.clarifai.com/blog/mcp-vs-a2a-clearly-explained
Merge Blog. "A2A vs MCP: how they overlap and differ." August 11, 2025. https://www.merge.dev/blog/mcp-vs-a2a
Oxylabs Blog. "MCP vs. A2A: AI Protocols Compared." 2025. https://oxylabs.io/blog/mcp-vs-a2a
Logto Blog. "A2A vs MCP: Two complementary protocols for the emerging agent ecosystem." April 11, 2025. https://blog.logto.io/a2a-mcp
LF AI & Data. "ACP Joins Forces with A2A." August 29, 2025. https://lfaidata.foundation/communityblog/2025/08/29/acp-joins-forces-with-a2a-under-the-linux-foundations-lf-ai-data/
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