Building Your First Multi-Agent System with the A2A Protocol

A Step-by-Step Guide to Agent Orchestration

Building a multi-agent system used to require extensive custom integration code, fragile message queues, and countless hours debugging communication failures. The landscape has changed dramatically with the emergence of standardized protocols that make agent orchestration accessible to development teams of all sizes. This tutorial walks you through creating your first collaborative agent system from scratch.

The A2A Protocol provides the foundation for this tutorial. We'll build a practical document processing pipeline where multiple specialized agents work together: one extracts text from PDFs, another performs sentiment analysis, and a third generates summary reports. By the end, you'll understand how to architect, implement, and deploy communicating agents in production environments.

Prerequisites and Environment Setup

Before diving into implementation, ensure you have the following tools installed:

• Python 3.9 or higher with pip package manager
• Docker for containerizing agents
• A message broker like RabbitMQ or Apache Kafka
• An A2A Protocol-compatible SDK (we'll use the open-source a2a-python library)

Install the required dependencies:

pip install a2a-python pdfplumber transformers

Set up your project structure with separate directories for each agent:

multi-agent-system/
├── agents/
│   ├── pdf_extractor/
│   ├── sentiment_analyzer/
│   └── report_generator/
├── shared/
│   └── models.py
└── docker-compose.yml

Step 1: Define Agent Capabilities

Each agent needs a clear capability definition that other agents can discover. Create a capability manifest that describes what your agent can do:

from a2a import AgentCapability

pdf_capability = AgentCapability(
    agent_id="pdf-extractor-001",
    name="PDF Text Extraction",
    description="Extracts text content from PDF documents",
    inputs=[{"type": "file", "format": "application/pdf"}],
    outputs=[{"type": "text", "format": "text/plain"}],
    version="1.0.0"
)

This manifest becomes part of the agent's registration with the A2A Protocol registry, allowing other agents to discover and invoke it dynamically.

Step 2: Implement the PDF Extractor Agent

Create the first agent that will receive document processing requests:

from a2a import Agent, Message
import pdfplumber

class PDFExtractorAgent(Agent):
    def __init__(self):
        super().__init__(capability=pdf_capability)

    async def handle_message(self, message: Message):
        pdf_path = message.payload["file_path"]

        with pdfplumber.open(pdf_path) as pdf:
            text = "\n".join(page.extract_text() for page in pdf.pages)

        # Send extracted text to next agent
        await self.send_message(
            recipient="sentiment-analyzer-001",
            payload={"text": text, "source": pdf_path}
        )

Step 3: Create the Sentiment Analyzer

The second agent receives text and performs analysis:

from transformers import pipeline

class SentimentAnalyzer(Agent):
    def __init__(self):
        super().__init__(capability=sentiment_capability)
        self.classifier = pipeline("sentiment-analysis")

    async def handle_message(self, message: Message):
        text = message.payload["text"]
        chunks = self._chunk_text(text, max_length=512)

        sentiments = [self.classifier(chunk)[0] for chunk in chunks]
        overall_sentiment = self._aggregate_sentiments(sentiments)

        await self.send_message(
            recipient="report-generator-001",
            payload={
                "sentiment": overall_sentiment,
                "source": message.payload["source"]
            }
        )

Step 4: Orchestrate the Workflow

With individual agents implemented, create an orchestrator that coordinates the entire pipeline. When building enterprise AI solutions, orchestration becomes critical for managing complex workflows:

from a2a import Orchestrator

orchestrator = Orchestrator()

# Define workflow as a directed graph
workflow = orchestrator.create_workflow([
    {"agent": "pdf-extractor-001", "next": ["sentiment-analyzer-001"]},
    {"agent": "sentiment-analyzer-001", "next": ["report-generator-001"]},
    {"agent": "report-generator-001", "next": []}
])

# Execute with error handling
result = await orchestrator.execute(
    workflow,
    initial_input={"file_path": "/documents/report.pdf"},
    timeout=300,
    retry_policy="exponential_backoff"
)

Step 5: Deploy and Monitor

Containerize each agent using Docker:

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "agent.py"]

Create a docker-compose configuration to run all agents together:

version: '3.8'
services:
  pdf-extractor:
    build: ./agents/pdf_extractor
    environment:
      - A2A_REGISTRY_URL=http://registry:8080

  sentiment-analyzer:
    build: ./agents/sentiment_analyzer
    environment:
      - A2A_REGISTRY_URL=http://registry:8080

Implement health checks and monitoring using the protocol's built-in telemetry:

from a2a import Telemetry

telemetry = Telemetry(agent=pdf_extractor)
telemetry.track_message_latency()
telemetry.track_error_rates()
telemetry.export_to_prometheus()

Testing Your Multi-Agent System

Thorough testing ensures reliability in production. Create integration tests that verify the entire pipeline:

import pytest
from a2a.testing import AgentTestHarness

@pytest.mark.asyncio
async def test_document_processing_pipeline():
    harness = AgentTestHarness([pdf_extractor, sentiment_analyzer, report_generator])

    result = await harness.execute(
        entry_point="pdf-extractor-001",
        input_data={"file_path": "test_document.pdf"}
    )

    assert result.status == "success"
    assert "sentiment" in result.output

Conclusion

You've now built a complete multi-agent system using standardized communication protocols. This architecture scales to handle hundreds of agents across distributed infrastructure, all coordinating through the same message-based interface. The modularity means you can swap agents, add new capabilities, or modify workflows without rewriting core logic.

As you expand your agent ecosystem, consider exploring advanced patterns like Computer-Using Agent Models that can interact directly with software interfaces. The combination of robust orchestration and advanced agent capabilities unlocks transformative automation possibilities for modern enterprises.