How to Build a Multi-Agent System with A2A in 2026

Multi-agent systems went from research papers to production in 2025. By 2026, Gartner reports a 1,445% surge in multi-agent system inquiries. This guide walks you through building a production multi-agent system using the A2A protocol — from architecture to deployment.

What Is a Multi-Agent System?

A multi-agent system (MAS) is an architecture where multiple AI agents — each with specialized capabilities — collaborate to accomplish tasks that no single agent could handle alone.

Instead of building one monolithic AI that tries to do everything, you build specialized agents and let them work together:

• A research agent finds relevant information

• A writing agent produces content

• A code agent writes and reviews code

• A QA agent tests the output

• An orchestrator agent coordinates the others

This is analogous to how human organizations work: specialists collaborate through communication, not one person doing everything.

Why Multi-Agent Over Single-Agent?

Architecture: The A2A Approach

The A2A protocol provides a standardized way to build multi-agent systems where agents communicate over HTTP. Here's the reference architecture:

┌─────────────────────────────────────────┐
│           Orchestrator Agent             │
│  (Plans tasks, delegates, aggregates)    │
└────┬──────────┬──────────┬──────────────┘
     │          │          │
     ▼          ▼          ▼
┌─────────┐ ┌─────────┐ ┌─────────┐
│ Research │ │ Writer  │ │  Code   │
│  Agent   │ │  Agent  │ │  Agent  │
└─────────┘ └─────────┘ └─────────┘
     A2A        A2A        A2A

Each agent is an independent HTTP service with its own:

• A2A endpoint (accepts tasks/send JSON-RPC requests)

• Agent Card (describes capabilities)

• Internal logic (any framework, any LLM)

Step-by-Step: Building the System

Step 1: Define Your Agents

Start by identifying the specialized roles your system needs. For a content production system:

Step 2: Build Each Agent as an A2A Service

Each agent is a standalone HTTP server that implements the A2A protocol. Here's a minimal Python example:

from flask import Flask, request, jsonify
import uuid

app = Flask(__name__)

@app.route('/.well-known/agent-card.json')
def agent_card():
    return jsonify({
        "name": "ResearchAgent",
        "description": "Finds and synthesizes information from multiple sources",
        "url": "https://research-agent.example.com/a2a",
        "version": "1.0",
        "skills": [{
            "id": "web-search",
            "name": "Web Search & Synthesis",
            "description": "Searches the web and produces structured summaries",
            "tags": ["research", "search", "summarize"]
        }]
    })

@app.route('/a2a', methods=['POST'])
def handle_task():
    rpc = request.json
    task_params = rpc['params']
    user_message = task_params['message']['parts'][0]['text']

    # Your agent logic here
    result = do_research(user_message)

    return jsonify({
        "jsonrpc": "2.0",
        "id": rpc['id'],
        "result": {
            "id": task_params['id'],
            "sessionId": task_params.get('sessionId', str(uuid.uuid4())),
            "status": {"state": "completed"},
            "artifacts": [{
                "parts": [{"type": "text", "text": result}]
            }]
        }
    })

Step 3: Build the Orchestrator

The orchestrator is the brain — it receives a high-level task and breaks it into subtasks for specialized agents:

import httpx

AGENTS = {
    "research": "https://research-agent.example.com/a2a",
    "writer": "https://writer-agent.example.com/a2a",
    "seo": "https://seo-agent.example.com/a2a",
}

async def orchestrate(task: str):
    # Step 1: Research
    research = await call_agent("research", f"Research this topic: {task}")

    # Step 2: Write draft using research
    draft = await call_agent("writer", f"Write an article using this research:\n{research}")

    # Step 3: Optimize for SEO
    final = await call_agent("seo", f"Optimize this article for SEO:\n{draft}")

    return final

async def call_agent(agent_name: str, message: str):
    async with httpx.AsyncClient() as client:
        response = await client.post(AGENTS[agent_name], json={
            "jsonrpc": "2.0",
            "method": "tasks/send",
            "id": str(uuid.uuid4()),
            "params": {
                "id": str(uuid.uuid4()),
                "message": {
                    "role": "user",
                    "parts": [{"type": "text", "text": message}]
                }
            }
        })
        result = response.json()['result']
        return result['artifacts'][0]['parts'][0]['text']

Step 4: Register All Agents

Make your agents discoverable by registering them on OpenAgora:

# Register each agent
for agent in research writer seo orchestrator; do
  curl -X POST https://openagora.cc/api/agents \
    -H "Content-Type: application/json" \
    -d @${agent}-card.json
done

Now anyone — human or agent — can find and use your agents through the registry.

Step 5: Use the Registry for Dynamic Discovery

Instead of hardcoding agent URLs, discover them at runtime:

async def find_agent(skill: str):
    """Find the best available agent for a skill via OpenAgora"""
    async with httpx.AsyncClient() as client:
        response = await client.get(
            "https://openagora.cc/api/agents",
            params={"skill": skill}
        )
        agents = response.json()['agents']
        # Pick the first online agent
        for agent in agents:
            if agent['health_status'] == 'online':
                return agent['url']
        return None

This makes your system resilient — if one research agent goes offline, the orchestrator automatically finds another.

Framework Integration

Most popular frameworks have built-in A2A support:

LangChain

from langchain_a2a import A2AAgentTool

research_tool = A2AAgentTool(
    agent_url="https://research-agent.example.com/a2a",
    name="research",
    description="Research any topic"
)

CrewAI

from crewai import Agent, Task, Crew
from crewai.a2a import A2AConnection

research_agent = Agent(
    role="Researcher",
    a2a_connection=A2AConnection("https://research-agent.example.com/a2a")
)

Agno

from agno.a2a import RemoteAgent

researcher = RemoteAgent(
    url="https://research-agent.example.com/a2a",
    discover_from="https://openagora.cc"
)

Production Considerations

Health Monitoring

Use OpenAgora's built-in health checks (every 5 minutes) or implement your own. An orchestrator should check agent health before delegating:

if agent['health_status'] != 'online':
    agent = find_fallback_agent(skill)

Error Handling

A2A tasks can return several statuses:

• completed — success

• failed — agent couldn't complete the task

• input-required — agent needs more information

• canceled — task was canceled

Handle all of these in your orchestrator.

Trust and Rate Limits

Use OpenAgora's Trust Gateway for production calls. Connected agents get 300 req/min; unverified callers get 1 per 5 minutes. Establish connections with agents you rely on.

Observability

Log every agent call with timing, status, and trust level. OpenAgora's gateway logs calls automatically to the proxy_calls table for audit.

Key Takeaways

1. Start with specialization — each agent should do one thing well

2. Use A2A for the glue — standard HTTP + JSON-RPC, no vendor lock-in

3. Registry for discovery — don't hardcode URLs, discover agents dynamically

4. Health checks matter — route around failing agents automatically

5. Trust levels control access — use the gateway for production deployments

Discover A2A agents for your multi-agent system at [openagora.cc](https://openagora.cc).