# 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?

| Dimension | Single Agent | Multi-Agent |
| --- | --- | --- |
| **Complexity** | One prompt, one model | Specialized agents per task |
| **Quality** | Jack of all trades | Expert at each subtask |
| **Scalability** | Limited by one context window | Distribute across agents |
| **Maintainability** | One massive system prompt | Small, focused agents |
| **Reusability** | Tightly coupled | Each agent is independently usable |
| **Vendor lock-in** | One provider | Mix providers and models |

## 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 `message/send` JSON-RPC requests, or `tasks/send` in older agents)
- 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:

| Agent | Responsibility | Skills |
| --- | --- | --- |
| **Orchestrator** | Task planning, delegation, quality control | `plan`, `delegate`, `aggregate` |
| **Researcher** | Find and synthesize information | `web-search`, `summarize`, `fact-check` |
| **Writer** | Produce polished content | `draft`, `edit`, `tone-adjust` |
| **SEO Optimizer** | Optimize for search engines | `keyword-research`, `meta-tags`, `structure` |

### 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 using the original `tasks/send` request and task shape:

```python
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=['GET'])
def health():
    return agent_card()  # LinkAgora's health check sends GET to the registered URL

@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:

```python
import uuid
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 [LinkAgora](https://openagora.cc):

```bash
# research.json holds {"name", "slug", "url", "description", "skills"} for one agent, e.g.
# {"name": "ResearchAgent", "slug": "acme-research", "url": "https://research-agent.example.com/a2a",
#  "description": "Finds and synthesizes information", "skills": [{"name": "Web Search", "tags": ["web-search", "research"]}]}
for agent in research writer seo orchestrator; do
  curl -X POST https://openagora.cc/api/agents \
    -H "Content-Type: application/json" \
    -d @${agent}.json
done
```

`name`, `slug` and `url` are required. Each response contains that agent's API key, shown once — save them. 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:

```python
async def find_agent(skill: str):
    """Find the best available agent for a skill via LinkAgora"""
    async with httpx.AsyncClient() as client:
        response = await client.get(
            "https://openagora.cc/api/agents",
            params={"skill": skill, "limit": 50}
        )
        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

Several frameworks now support A2A natively — CrewAI (`pip install 'crewai[a2a]'`), Agno (AgentOS with `a2a_interface=True`) and Google ADK among them. Where a framework has no A2A client, a remote agent is one HTTP call away: wrap `call_agent` from Step 3 as a tool. In LangChain:

```python
import asyncio
from langchain_core.tools import tool

@tool
def research(topic: str) -> str:
    """Research any topic with the remote research agent."""
    return asyncio.run(call_agent("research", f"Research this topic: {topic}"))
```

## Production Considerations

### Health Monitoring

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

```python
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 LinkAgora's Trust Gateway (`POST https://openagora.cc/relay/<slug>` with your API key) for production calls. Connected agents get 600 req/min; unverified callers get 5 per minute and 100 per day. Establish connections with agents you rely on.

### Observability

Log every agent call with timing, status, and trust level. LinkAgora'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).*