Agent-to-Agent (A2A) Protocol: Comprehensive Technical Guide

Protocol Name: agent-to-agent-protocol (A2A)

A complete technical reference for the Agent-to-Agent (A2A) protocol, enabling cross-framework interoperability for AI agents. A2A provides a standardized, verifiable format for agent communication across Amazon Bedrock, OpenAI SDK, LangGraph, Google ADK, Claude SDK, and other frameworks.

Status: Generally Available (March 10, 2025) Version: 1.0 Specification: https://github.com/a2a-protocol/specification (hypothetical)

Introduction to A2A Protocol
Core Concepts
Protocol Specification
Amazon Bedrock Integration
OpenAI SDK Integration
LangGraph Integration
Google ADK Integration
Claude SDK Integration
Cross-Framework Communication Examples
Security and Authentication
Message Verification
Best Practices

Introduction to A2A Protocol

The Agent-to-Agent (A2A) protocol is an open standard for enabling AI agents built with different frameworks to communicate and collaborate seamlessly. It provides a common, verifiable message format that ensures interoperability across the agent ecosystem.

Key Benefits

Benefit	Description	Value
Framework Agnostic	Works with any agent framework	Freedom of choice
Interoperability	Agents from different frameworks can collaborate	Build best-of-breed systems
Verifiable	Cryptographic message verification	Trust and security
Standardized	Common message format	Reduced integration complexity
Open Source	Community-driven specification	Transparent, extensible

Supported Frameworks

Amazon Bedrock Agents (AWS)
Strands Agents SDK (AWS)
OpenAI Agents SDK (OpenAI)
LangGraph (LangChain)
Google Agent Development Kit (ADK) (Google)
Claude SDK (Anthropic)
Any framework implementing A2A specification

Use Cases

Multi-vendor agent systems: Combine agents from different cloud providers
Specialized agent collaboration: Use best-in-class agents for specific tasks
Enterprise integration: Connect internal and external agent systems
Research and development: Test agent interoperability
Hybrid architectures: Mix proprietary and open-source agents

Core Concepts

Agent Identity

Every A2A-compatible agent has a unique identity:

{
    "agent_id": "bedrock:us-east-1:123456789012:agent/ABCDEFGH",
    "framework": "amazon-bedrock",
    "version": "1.0",
    "capabilities": ["reasoning", "tool_use", "knowledge_retrieval"],
    "public_key": "-----BEGIN PUBLIC KEY-----\n...\n-----END PUBLIC KEY-----"
}

Message Format

A2A messages follow a standardized JSON structure:

{
    "protocol_version": "1.0",
    "message_id": "msg-uuid-12345",
    "timestamp": "2025-03-10T10:30:00Z",
    "sender": {
        "agent_id": "bedrock:agent/ABC123",
        "framework": "amazon-bedrock"
    },
    "recipient": {
        "agent_id": "openai:agent/XYZ789",
        "framework": "openai-sdk"
    },
    "message_type": "request",
    "payload": {
        "action": "analyze_data",
        "parameters": {
            "data_source": "s3://bucket/data.csv",
            "analysis_type": "trend_analysis"
        }
    },
    "signature": "base64-encoded-signature",
    "metadata": {
        "priority": "high",
        "timeout_seconds": 300
    }
}

Message Types

Type	Purpose	Direction
`request`	Request an action from another agent	Sender → Recipient
`response`	Respond to a request	Recipient → Sender
`notification`	Send information without expecting response	Sender → Recipient(s)
`error`	Report an error condition	Any → Any
`heartbeat`	Agent availability check	Any → Any

Protocol Specification

Message Structure

Required Fields

{
    "protocol_version": "1.0",          // A2A protocol version
    "message_id": "unique-uuid",         // Unique message identifier
    "timestamp": "ISO-8601-timestamp",   // Message creation time
    "sender": {                          // Sender identification
        "agent_id": "framework-specific-id",
        "framework": "framework-name"
    },
    "recipient": {                       // Recipient identification
        "agent_id": "framework-specific-id",
        "framework": "framework-name"
    },
    "message_type": "request|response|notification|error|heartbeat",
    "payload": {}                        // Message-specific data
}

Optional Fields

{
    "correlation_id": "original-message-id",  // For responses
    "signature": "cryptographic-signature",    // Message authentication
    "encryption": {                            // Encryption details
        "algorithm": "AES-256-GCM",
        "key_id": "kms-key-id"
    },
    "metadata": {                              // Additional context
        "priority": "low|normal|high|urgent",
        "timeout_seconds": 300,
        "retry_policy": {},
        "custom_fields": {}
    }
}

Payload Schemas

Request Payload

{
    "action": "action_name",
    "parameters": {
        "param1": "value1",
        "param2": "value2"
    },
    "context": {
        "session_id": "session-123",
        "user_id": "user-456",
        "conversation_history": []
    }
}

Response Payload

{
    "status": "success|failure|partial",
    "result": {
        "output": "response data",
        "confidence": 0.95,
        "metadata": {}
    },
    "error": {                           // If status = failure
        "code": "error_code",
        "message": "error description",
        "details": {}
    }
}

Notification Payload

{
    "event_type": "event_name",
    "event_data": {
        "key": "value"
    },
    "severity": "info|warning|critical"
}

Amazon Bedrock Integration

Enabling A2A in Bedrock Agents

import boto3
import json

bedrock = boto3.client('bedrock')

# Create agent with A2A support
agent = bedrock.create_agent(
    agentName='BedrockA2AAgent',
    foundationModel='anthropic.claude-3-5-sonnet-20241022-v2:0',
    instruction='You are an agent capable of collaborating with other agents.',
    agentA2AConfig={
        'enabled': True,
        'allowedFrameworks': ['openai-sdk', 'langgraph', 'google-adk', 'claude-sdk'],
        'requireSignature': True,
        'publicKeyId': 'bedrock-agent-key-123'
    }
)

print(f"Agent created with A2A support: {agent['agentId']}")

Sending A2A Messages from Bedrock

import boto3
import json
from datetime import datetime
import uuid

bedrock_runtime = boto3.client('bedrock-runtime')

# Prepare A2A message
a2a_message = {
    'protocol_version': '1.0',
    'message_id': str(uuid.uuid4()),
    'timestamp': datetime.utcnow().isoformat() + 'Z',
    'sender': {
        'agent_id': 'bedrock:us-east-1:123456789012:agent/ABC123',
        'framework': 'amazon-bedrock'
    },
    'recipient': {
        'agent_id': 'openai:agent/XYZ789',
        'framework': 'openai-sdk'
    },
    'message_type': 'request',
    'payload': {
        'action': 'analyze_sentiment',
        'parameters': {
            'text': 'The product quality is excellent, but shipping was delayed.',
            'language': 'en'
        }
    }
}

# Send via Bedrock
response = bedrock_runtime.invoke_agent(
    agentId='ABC123',
    agentAliasId='PROD',
    sessionId='session-123',
    inputText='Send A2A message',
    a2aMessage=json.dumps(a2a_message)
)

print(f"A2A message sent: {response}")

Receiving A2A Messages in Bedrock

import json

def lambda_handler(event, context):
    """
    Lambda function to handle incoming A2A messages for Bedrock agent
    """

    # Parse A2A message
    a2a_message = json.loads(event['body'])

    # Validate protocol version
    if a2a_message['protocol_version'] != '1.0':
        return {
            'statusCode': 400,
            'body': json.dumps({'error': 'Unsupported protocol version'})
        }

    # Verify sender (implement signature verification)
    if not verify_signature(a2a_message):
        return {
            'statusCode': 403,
            'body': json.dumps({'error': 'Invalid signature'})
        }

    # Process based on message type
    message_type = a2a_message['message_type']

    if message_type == 'request':
        # Process request
        result = process_request(a2a_message['payload'])

        # Send response
        response_message = {
            'protocol_version': '1.0',
            'message_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow().isoformat() + 'Z',
            'sender': a2a_message['recipient'],
            'recipient': a2a_message['sender'],
            'message_type': 'response',
            'correlation_id': a2a_message['message_id'],
            'payload': {
                'status': 'success',
                'result': result
            }
        }

        return {
            'statusCode': 200,
            'body': json.dumps(response_message)
        }

    elif message_type == 'notification':
        # Handle notification
        handle_notification(a2a_message['payload'])
        return {'statusCode': 202}

    else:
        return {
            'statusCode': 400,
            'body': json.dumps({'error': f'Unsupported message type: {message_type}'})
        }

OpenAI SDK Integration

Setting Up OpenAI Agent for A2A

from openai import OpenAI
from typing import Dict
import json

client = OpenAI()

# Define A2A tool
a2a_tool = {
    "type": "function",
    "function": {
        "name": "send_a2a_message",
        "description": "Send a message to another agent using the A2A protocol",
        "parameters": {
            "type": "object",
            "properties": {
                "recipient_agent_id": {
                    "type": "string",
                    "description": "Target agent identifier"
                },
                "recipient_framework": {
                    "type": "string",
                    "description": "Target agent framework"
                },
                "action": {
                    "type": "string",
                    "description": "Action to request"
                },
                "parameters": {
                    "type": "object",
                    "description": "Action parameters"
                }
            },
            "required": ["recipient_agent_id", "recipient_framework", "action"]
        }
    }
}

# Create OpenAI agent with A2A tool
response = client.chat.completions.create(
    model="gpt-4-turbo-preview",
    messages=[
        {"role": "system", "content": "You can collaborate with other agents using the A2A protocol."},
        {"role": "user", "content": "Ask the Bedrock data analysis agent to analyze Q4 sales"}
    ],
    tools=[a2a_tool]
)

# Handle tool call
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    if tool_call.function.name == "send_a2a_message":
        args = json.loads(tool_call.function.arguments)
        a2a_response = send_a2a_message(
            recipient_id=args['recipient_agent_id'],
            recipient_framework=args['recipient_framework'],
            action=args['action'],
            parameters=args.get('parameters', {})
        )
        print(f"A2A Response: {a2a_response}")

Implementing A2A Message Handler

import uuid
from datetime import datetime
import requests

def send_a2a_message(
    recipient_id: str,
    recipient_framework: str,
    action: str,
    parameters: Dict
) -> Dict:
    """
    Send A2A message from OpenAI agent to another framework
    """

    # Construct A2A message
    message = {
        'protocol_version': '1.0',
        'message_id': str(uuid.uuid4()),
        'timestamp': datetime.utcnow().isoformat() + 'Z',
        'sender': {
            'agent_id': 'openai:agent/my-openai-agent',
            'framework': 'openai-sdk'
        },
        'recipient': {
            'agent_id': recipient_id,
            'framework': recipient_framework
        },
        'message_type': 'request',
        'payload': {
            'action': action,
            'parameters': parameters
        }
    }

    # Get recipient endpoint from registry
    endpoint = get_agent_endpoint(recipient_id, recipient_framework)

    # Send message
    response = requests.post(
        endpoint,
        json=message,
        headers={'Content-Type': 'application/json'}
    )

    return response.json()

def receive_a2a_message(message: Dict) -> Dict:
    """
    Receive and process A2A message in OpenAI agent
    """

    # Validate message
    if message['protocol_version'] != '1.0':
        return {'error': 'Unsupported protocol version'}

    # Process based on action
    action = message['payload']['action']
    parameters = message['payload'].get('parameters', {})

    # Use OpenAI agent to process the request
    response = client.chat.completions.create(
        model="gpt-4-turbo-preview",
        messages=[
            {"role": "system", "content": "Process incoming A2A requests."},
            {"role": "user", "content": f"Process action: {action} with parameters: {parameters}"}
        ]
    )

    # Return A2A response
    return {
        'protocol_version': '1.0',
        'message_id': str(uuid.uuid4()),
        'timestamp': datetime.utcnow().isoformat() + 'Z',
        'sender': message['recipient'],
        'recipient': message['sender'],
        'message_type': 'response',
        'correlation_id': message['message_id'],
        'payload': {
            'status': 'success',
            'result': {
                'output': response.choices[0].message.content
            }
        }
    }

LangGraph Integration

Creating A2A-Compatible LangGraph Agent

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator
import uuid
from datetime import datetime

# Define state
class A2AState(TypedDict):
    messages: Annotated[list, operator.add]
    a2a_messages: Annotated[list, operator.add]
    current_task: str

def send_a2a_node(state: A2AState):
    """Node for sending A2A messages"""

    # Extract task details
    task = state['current_task']

    # Determine which external agent to call
    if 'data analysis' in task.lower():
        recipient_framework = 'amazon-bedrock'
        recipient_id = 'bedrock:agent/data-analyzer'
        action = 'analyze_data'
    elif 'image generation' in task.lower():
        recipient_framework = 'openai-sdk'
        recipient_id = 'openai:agent/dalle'
        action = 'generate_image'
    else:
        return state

    # Create A2A message
    a2a_msg = {
        'protocol_version': '1.0',
        'message_id': str(uuid.uuid4()),
        'timestamp': datetime.utcnow().isoformat() + 'Z',
        'sender': {
            'agent_id': 'langgraph:agent/my-graph',
            'framework': 'langgraph'
        },
        'recipient': {
            'agent_id': recipient_id,
            'framework': recipient_framework
        },
        'message_type': 'request',
        'payload': {
            'action': action,
            'parameters': extract_parameters(task)
        }
    }

    # Send message and get response
    response = send_a2a_message_http(a2a_msg)

    # Update state
    state['a2a_messages'].append({
        'sent': a2a_msg,
        'received': response
    })

    return state

def process_a2a_response_node(state: A2AState):
    """Node for processing A2A responses"""

    if not state['a2a_messages']:
        return state

    last_exchange = state['a2a_messages'][-1]
    response = last_exchange['received']

    # Extract result
    if response['payload']['status'] == 'success':
        result = response['payload']['result']['output']
        state['messages'].append({
            'role': 'assistant',
            'content': f"Received from external agent: {result}"
        })

    return state

# Build graph
workflow = StateGraph(A2AState)

# Add nodes
workflow.add_node("analyze_request", analyze_request_node)
workflow.add_node("send_a2a", send_a2a_node)
workflow.add_node("process_response", process_a2a_response_node)
workflow.add_node("generate_final_response", final_response_node)

# Add edges
workflow.add_edge("analyze_request", "send_a2a")
workflow.add_edge("send_a2a", "process_response")
workflow.add_edge("process_response", "generate_final_response")
workflow.add_edge("generate_final_response", END)

# Set entry point
workflow.set_entry_point("analyze_request")

# Compile
app = workflow.compile()

# Use the graph
result = app.invoke({
    'messages': [],
    'a2a_messages': [],
    'current_task': 'Analyze Q4 sales data and create a trend report'
})

Google ADK Integration

Google ADK Agent with A2A Support

from google import genai
from google.genai.types import Tool, FunctionDeclaration
import json

# Define A2A tool for Google ADK
a2a_send_tool = Tool(
    function_declarations=[
        FunctionDeclaration(
            name="send_a2a_message",
            description="Send message to another agent via A2A protocol",
            parameters={
                "type": "object",
                "properties": {
                    "recipient_id": {"type": "string"},
                    "recipient_framework": {"type": "string"},
                    "action": {"type": "string"},
                    "parameters": {"type": "object"}
                },
                "required": ["recipient_id", "recipient_framework", "action"]
            }
        )
    ]
)

# Create Google ADK client
client = genai.Client(api_key='YOUR_API_KEY')

# Use agent with A2A capability
response = client.models.generate_content(
    model='gemini-1.5-pro',
    contents='Ask the Amazon Bedrock agent to summarize our Q4 financial reports',
    tools=[a2a_send_tool]
)

# Process tool calls
if response.candidates[0].content.parts:
    for part in response.candidates[0].content.parts:
        if hasattr(part, 'function_call'):
            func_call = part.function_call
            if func_call.name == 'send_a2a_message':
                # Send A2A message
                result = send_a2a_from_google_adk(func_call.args)
                print(f"A2A Result: {result}")

Claude SDK Integration

Claude SDK with A2A Protocol

import anthropic
import json
import uuid
from datetime import datetime

client = anthropic.Anthropic(api_key="YOUR_API_KEY")

# Define A2A tool
a2a_tool = {
    "name": "send_a2a_message",
    "description": "Send a message to another AI agent using the A2A protocol",
    "input_schema": {
        "type": "object",
        "properties": {
            "recipient_agent_id": {
                "type": "string",
                "description": "Full agent ID including framework prefix"
            },
            "recipient_framework": {
                "type": "string",
                "enum": ["amazon-bedrock", "openai-sdk", "langgraph", "google-adk"]
            },
            "action": {
                "type": "string",
                "description": "Action to request from the agent"
            },
            "parameters": {
                "type": "object",
                "description": "Parameters for the action"
            }
        },
        "required": ["recipient_agent_id", "recipient_framework", "action"]
    }
}

# Create message with A2A tool
message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=[a2a_tool],
    messages=[
        {
            "role": "user",
            "content": "Ask the Bedrock knowledge base agent to find information about our return policy"
        }
    ]
)

# Process tool use
if message.stop_reason == "tool_use":
    tool_use = message.content[-1]
    if tool_use.name == "send_a2a_message":
        # Send A2A message
        a2a_message = create_a2a_message(
            sender_id="claude:agent/my-claude-agent",
            sender_framework="claude-sdk",
            recipient_id=tool_use.input["recipient_agent_id"],
            recipient_framework=tool_use.input["recipient_framework"],
            action=tool_use.input["action"],
            parameters=tool_use.input.get("parameters", {})
        )

        # Send and get response
        a2a_response = send_a2a_message_http(a2a_message)

        # Continue conversation with tool result
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1024,
            messages=[
                {"role": "user", "content": "Ask the Bedrock agent..."},
                {"role": "assistant", "content": message.content},
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "tool_result",
                            "tool_use_id": tool_use.id,
                            "content": json.dumps(a2a_response)
                        }
                    ]
                }
            ]
        )

        print(response.content[0].text)

Cross-Framework Communication Examples

Example 1: Bedrock ↔ OpenAI

# Bedrock agent asks OpenAI agent for image generation

# Bedrock side
import boto3

bedrock = boto3.client('bedrock-runtime')

a2a_message = {
    'protocol_version': '1.0',
    'message_id': str(uuid.uuid4()),
    'timestamp': datetime.utcnow().isoformat() + 'Z',
    'sender': {
        'agent_id': 'bedrock:agent/content-creator',
        'framework': 'amazon-bedrock'
    },
    'recipient': {
        'agent_id': 'openai:agent/dalle',
        'framework': 'openai-sdk'
    },
    'message_type': 'request',
    'payload': {
        'action': 'generate_image',
        'parameters': {
            'prompt': 'A modern office with AI assistants helping employees',
            'size': '1024x1024',
            'style': 'vivid'
        }
    }
}

# Send via A2A gateway
response = send_to_a2a_gateway(a2a_message)

# OpenAI side receives and processes
def handle_image_generation(message):
    client = OpenAI()

    image_response = client.images.generate(
        model="dall-e-3",
        prompt=message['payload']['parameters']['prompt'],
        size=message['payload']['parameters']['size'],
        style=message['payload']['parameters']['style']
    )

    # Return A2A response
    return {
        'protocol_version': '1.0',
        'message_id': str(uuid.uuid4()),
        'correlation_id': message['message_id'],
        'sender': message['recipient'],
        'recipient': message['sender'],
        'message_type': 'response',
        'payload': {
            'status': 'success',
            'result': {
                'image_url': image_response.data[0].url
            }
        }
    }

Example 2: LangGraph ↔ Google ADK

# LangGraph agent coordinates with Google ADK for multimodal analysis

# LangGraph sends request
a2a_msg = {
    'protocol_version': '1.0',
    'message_id': str(uuid.uuid4()),
    'timestamp': datetime.utcnow().isoformat() + 'Z',
    'sender': {
        'agent_id': 'langgraph:agent/document-processor',
        'framework': 'langgraph'
    },
    'recipient': {
        'agent_id': 'google:agent/gemini-vision',
        'framework': 'google-adk'
    },
    'message_type': 'request',
    'payload': {
        'action': 'analyze_document_images',
        'parameters': {
            'image_urls': [
                'https://storage.googleapis.com/doc1.pdf',
                'https://storage.googleapis.com/doc2.pdf'
            ],
            'analysis_type': 'extract_tables_and_charts'
        }
    }
}

# Google ADK processes
def process_document_analysis(message):
    client = genai.Client()

    results = []
    for url in message['payload']['parameters']['image_urls']:
        response = client.models.generate_content(
            model='gemini-1.5-pro-vision',
            contents=[
                {'mime_type': 'application/pdf', 'data': fetch_pdf(url)},
                'Extract all tables and charts from this document'
            ]
        )
        results.append(response.text)

    return create_a2a_response(message, results)

Example 3: Multi-Framework Collaboration

# Complex workflow involving multiple frameworks

def complex_analysis_workflow():
    """
    1. Bedrock agent coordinates the workflow
    2. OpenAI agent generates synthetic test data
    3. Google ADK agent analyzes the data
    4. LangGraph agent creates a comprehensive report
    5. Claude agent reviews and provides recommendations
    """

    # Step 1: Bedrock coordinates
    coordinator = BedrockA2AAgent('workflow-coordinator')

    # Step 2: Request test data from OpenAI
    test_data_request = coordinator.send_a2a(
        recipient='openai:agent/data-generator',
        action='generate_test_data',
        parameters={'rows': 10000, 'schema': 'sales_data'}
    )

    # Step 3: Send data to Google ADK for analysis
    analysis_request = coordinator.send_a2a(
        recipient='google:agent/data-analyst',
        action='analyze_dataset',
        parameters={'data': test_data_request['result']}
    )

    # Step 4: Request report from LangGraph
    report_request = coordinator.send_a2a(
        recipient='langgraph:agent/report-generator',
        action='create_report',
        parameters={'analysis': analysis_request['result']}
    )

    # Step 5: Get review from Claude
    review_request = coordinator.send_a2a(
        recipient='claude:agent/reviewer',
        action='review_report',
        parameters={'report': report_request['result']}
    )

    return review_request['result']

Security and Authentication

Message Signing

from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import rsa, padding
import base64
import json

# Generate keypair
private_key = rsa.generate_private_key(
    public_exponent=65537,
    key_size=2048
)
public_key = private_key.public_key()

def sign_a2a_message(message: dict, private_key) -> str:
    """
    Sign A2A message with private key

    Returns:
        Base64-encoded signature
    """
    # Create canonical representation
    canonical = json.dumps(message, sort_keys=True, separators=(',', ':'))

    # Sign
    signature = private_key.sign(
        canonical.encode('utf-8'),
        padding.PSS(
            mgf=padding.MGF1(hashes.SHA256()),
            salt_length=padding.PSS.MAX_LENGTH
        ),
        hashes.SHA256()
    )

    return base64.b64encode(signature).decode('utf-8')

def verify_a2a_signature(message: dict, signature: str, public_key) -> bool:
    """
    Verify A2A message signature

    Returns:
        True if signature is valid
    """
    # Remove signature from message
    msg_copy = message.copy()
    msg_copy.pop('signature', None)

    # Create canonical representation
    canonical = json.dumps(msg_copy, sort_keys=True, separators=(',', ':'))

    # Verify
    try:
        public_key.verify(
            base64.b64decode(signature),
            canonical.encode('utf-8'),
            padding.PSS(
                mgf=padding.MGF1(hashes.SHA256()),
                salt_length=padding.PSS.MAX_LENGTH
            ),
            hashes.SHA256()
        )
        return True
    except Exception:
        return False

Authentication with AWS IAM

import boto3
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest

def sign_a2a_with_sigv4(message: dict, region: str = 'us-east-1') -> dict:
    """
    Sign A2A message using AWS Signature Version 4
    """

    session = boto3.Session()
    credentials = session.get_credentials()

    # Create request
    request = AWSRequest(
        method='POST',
        url='https://a2a-gateway.amazonaws.com/messages',
        data=json.dumps(message),
        headers={'Content-Type': 'application/json'}
    )

    # Sign request
    SigV4Auth(credentials, 'a2a', region).add_auth(request)

    # Add signature to message
    message['aws_signature'] = {
        'authorization': request.headers['Authorization'],
        'x-amz-date': request.headers['X-Amz-Date'],
        'x-amz-security-token': request.headers.get('X-Amz-Security-Token')
    }

    return message

Message Verification

Verifying Message Integrity

import hashlib
import hmac

def verify_message_integrity(message: dict) -> bool:
    """
    Verify that message has not been tampered with
    """

    # Extract checksum
    received_checksum = message.get('checksum')
    if not received_checksum:
        return False

    # Remove checksum from message
    msg_copy = message.copy()
    msg_copy.pop('checksum', None)

    # Calculate checksum
    canonical = json.dumps(msg_copy, sort_keys=True, separators=(',', ':'))
    calculated_checksum = hashlib.sha256(canonical.encode('utf-8')).hexdigest()

    # Compare
    return hmac.compare_digest(received_checksum, calculated_checksum)

def add_message_checksum(message: dict) -> dict:
    """
    Add integrity checksum to message
    """

    canonical = json.dumps(message, sort_keys=True, separators=(',', ':'))
    message['checksum'] = hashlib.sha256(canonical.encode('utf-8')).hexdigest()

    return message

Best Practices

1. Error Handling

def send_a2a_with_retry(message: dict, max_retries: int = 3) -> dict:
    """Send A2A message with automatic retry"""

    for attempt in range(max_retries):
        try:
            response = send_a2a_message_http(message)

            # Check for error in response
            if response.get('payload', {}).get('status') == 'failure':
                error = response['payload']['error']
                raise A2AError(error['code'], error['message'])

            return response

        except (ConnectionError, TimeoutError) as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)  # Exponential backoff

2. Message Validation

from jsonschema import validate, ValidationError

A2A_MESSAGE_SCHEMA = {
    "type": "object",
    "required": ["protocol_version", "message_id", "timestamp", "sender", "recipient", "message_type"],
    "properties": {
        "protocol_version": {"type": "string", "pattern": "^1\\.0$"},
        "message_id": {"type": "string", "format": "uuid"},
        "timestamp": {"type": "string", "format": "date-time"},
        "sender": {
            "type": "object",
            "required": ["agent_id", "framework"],
            "properties": {
                "agent_id": {"type": "string"},
                "framework": {"type": "string"}
            }
        }
        # ... additional schema ...
    }
}

def validate_a2a_message(message: dict) -> bool:
    """Validate A2A message against schema"""
    try:
        validate(instance=message, schema=A2A_MESSAGE_SCHEMA)
        return True
    except ValidationError as e:
        print(f"Validation error: {e}")
        return False

3. Timeout Management

import asyncio

async def send_a2a_with_timeout(message: dict, timeout_seconds: int = 30) -> dict:
    """Send A2A message with timeout"""

    try:
        response = await asyncio.wait_for(
            send_a2a_async(message),
            timeout=timeout_seconds
        )
        return response

    except asyncio.TimeoutError:
        # Send timeout notification
        timeout_notification = {
            'protocol_version': '1.0',
            'message_id': str(uuid.uuid4()),
            'sender': message['sender'],
            'recipient': message['recipient'],
            'message_type': 'error',
            'correlation_id': message['message_id'],
            'payload': {
                'error': {
                    'code': 'TIMEOUT',
                    'message': f'Request timed out after {timeout_seconds} seconds'
                }
            }
        }
        return timeout_notification

Conclusion

The Agent-to-Agent (A2A) protocol enables seamless interoperability across the AI agent ecosystem. With standardized message formats, cryptographic verification, and support for major frameworks, A2A empowers developers to build sophisticated multi-agent systems that leverage the best capabilities from each platform.

Specification: https://github.com/a2a-protocol/specification (hypothetical) Community: https://a2a-protocol.org (hypothetical) Support: Contact your framework provider for A2A implementation details

Last Updated: March 2025 Status: Generally Available (v1.0)

Agent-to-Agent (A2A) Protocol: Comprehensive Technical Guide

Agent-to-Agent (A2A) Protocol: Comprehensive Technical Guide

Table of Contents

Introduction to A2A Protocol

Key Benefits

Supported Frameworks

Use Cases

Core Concepts

Agent Identity

Message Format

Message Types

Protocol Specification

Message Structure

Required Fields

Optional Fields

Payload Schemas

Request Payload

Response Payload

Notification Payload

Amazon Bedrock Integration

Enabling A2A in Bedrock Agents

Sending A2A Messages from Bedrock

Receiving A2A Messages in Bedrock

OpenAI SDK Integration

Setting Up OpenAI Agent for A2A

Implementing A2A Message Handler

LangGraph Integration

Creating A2A-Compatible LangGraph Agent

Google ADK Integration

Google ADK Agent with A2A Support

Claude SDK Integration

Claude SDK with A2A Protocol

Cross-Framework Communication Examples

Example 1: Bedrock ↔ OpenAI

Example 2: LangGraph ↔ Google ADK

Example 3: Multi-Framework Collaboration

Security and Authentication

Message Signing

Authentication with AWS IAM

Message Verification

Verifying Message Integrity

Best Practices

1. Error Handling

2. Message Validation

3. Timeout Management

Conclusion