Agent-to-Agent (A2A): Zero-to-Production

This guide is a comprehensive, production-grade walkthrough for building Agent-to-Agent (A2A) systems — from first principles to real-world deployment. It is written for engineers who already understand APIs, cloud infrastructure, and LLMs, but are new to multi-agent interoperability.

The focus is on practical engineering, not demos.

1. What Is Agent-to-Agent (A2A)?

A2A (Agent-to-Agent) is an architectural pattern and emerging protocol standard that enables autonomous software agents to:

Discover each other
Advertise capabilities
Exchange structured tasks
Stream intermediate progress
Exchange artifacts and results
Operate independently across services, teams, or organizations

Think of A2A as:

HTTP + JSON-RPC + Contracts + Autonomy

Where REST is for services and RPC is for procedures, A2A is for goals.

2. Why A2A Exists (The Problem It Solves)

Single-agent systems break down when:

Tasks exceed a single context window
Domains require specialist reasoning
Parallelism is required
Reliability and verification matter
Teams want composability across vendors

A2A introduces:

Problem	A2A Solution
Monolithic agent logic	Role-specialized agents
Tight coupling	Capability contracts
Hidden prompts	Explicit task schemas
Non-determinism	Critic & verifier agents
Vendor lock-in	Protocol-level interoperability

3. Core A2A Concepts (Non-Negotiable)

3.1 Agents

An agent is a long-running service that:

Accepts tasks
Executes autonomously
Communicates via the A2A protocol

Agents are services, not scripts.

3.2 Agent Card

The Agent Card is the agent’s public contract.

It defines:

Identity
Capabilities
Endpoints
Protocol version

Example:

{
  "name": "research-agent",
  "description": "Finds, summarizes, and cites technical sources",
  "version": "1.0.0",
  "protocol": "a2a/0.1",
  "endpoint": "https://agents.example.com/research",
  "capabilities": [
    "web_research",
    "citation_generation",
    "technical_summary"
  ]
}

Conventionally hosted at:

/.well-known/agent-card.json

3.3 Tasks

A task is a goal-oriented request, not a function call.

Key properties:

Unique task ID
Clear objective
Optional constraints
Expected artifacts

Example:

{
  "task_id": "task-123",
  "objective": "Summarize A2A protocol security requirements",
  "constraints": {
    "max_tokens": 800,
    "citations_required": true
  }
}

3.4 Streaming & Progress Updates

A2A supports long-running tasks.

Agents can stream:

Status updates
Partial results
Intermediate artifacts

This is essential for:

UX responsiveness
Timeout avoidance
Observability

4. Reference Architecture

A minimal production A2A system:

┌──────────────┐
│ Orchestrator │
└──────┬───────┘
       │ discovers
┌──────▼───────┐
│ Agent Cards  │
└──────┬───────┘
       │ tasks
┌──────▼─────────────┐
│ Specialist Agents  │
│  - Research        │
│  - Planning        │
│  - Coding          │
│  - Verification    │
└────────────────────┘

5. Choosing a Tech Stack

5.1 Languages

Best choices today:

Python (fastest ecosystem growth)
TypeScript / Node.js (edge & web-native)
.NET (strong enterprise + Azure support)
Go (high-throughput, low-latency agents)

5.2 Transport

HTTPS (mandatory)
JSON-RPC 2.0 semantics
Server-Sent Events (SSE) or WebSockets for streaming

5.3 LLM Integration

Agents typically wrap:

OpenAI-compatible APIs
Local models
Hybrid tool + LLM logic

LLMs are internal to agents — never exposed directly.

6. Building Your First A2A Agent (Python)

6.1 Minimal HTTP Server

from fastapi import FastAPI, Request

app = FastAPI()

@app.post("/a2a")
async def handle_task(request: Request):
    payload = await request.json()
    return {
        "task_id": payload.get("task_id"),
        "status": "completed",
        "result": "Hello from A2A agent"
    }

6.2 Publishing the Agent Card

@app.get("/.well-known/agent-card.json")
async def agent_card():
    return {
        "name": "hello-agent",
        "version": "0.1.0",
        "endpoint": "/a2a",
        "capabilities": ["demo"]
    }

7. Orchestrator Agent (Coordinator Pattern)

The orchestrator:

Discovers agents
Selects by capability
Dispatches tasks
Aggregates results

Patterns:

Planner → Executor
Leader → Workers
Debate → Consensus
Market-based bidding

8. Security (Production-Critical)

8.1 Transport Security

TLS 1.2+ (1.3 recommended)
HTTPS only

8.2 Authentication

Common approaches:

mTLS (best for internal systems)
OAuth2 / OIDC tokens
Cloud-managed identities

8.3 Authorization

Agents must validate:

Who is calling
What capability is being requested
Rate limits

Never trust agent input blindly.

9. Observability & Reliability

9.1 Logging

Log:

Task ID
Agent ID
Duration
Errors

9.2 Metrics

Track:

Task latency
Success / failure rate
Token usage
Cost per task

9.3 Tracing

Use OpenTelemetry to trace tasks across agents.

10. Testing A2A Systems

10.1 Unit Testing

Task parsing
Capability routing

10.2 Integration Testing

Multi-agent workflows
Failure injection

10.3 Chaos Testing

Kill agents randomly.

Your system should degrade gracefully.

11. Deployment

11.1 Containerization

Docker per agent
Immutable builds

11.2 Orchestration

Kubernetes
Nomad
Cloud App Services

11.3 Scaling

Scale agents independently.

Avoid scaling orchestrators blindly.

12. Common Anti-Patterns

❌ Too many agents ❌ Hidden prompts ❌ No verification agent ❌ No cost controls ❌ Tight coupling

13. Production Maturity Checklist

Agent cards published
Auth enabled
Rate limits enforced
Streaming supported
Observability live
Failure recovery tested

14. Recommended Learning Path

Single agent
Two-agent planner/executor
Add critic agent
Add streaming
Add auth
Deploy

15. Resources & Further Reading

Protocol & Specs

A2A Protocol Specification – https://a2a-protocol.org

Frameworks

AutoGen (Microsoft)
LangGraph (LangChain)
CrewAI

Cloud & Enterprise

Azure Agent Framework (A2A)
Kubernetes Patterns for AI Agents

Research

Multi-Agent Systems (MAS)
Blackboard Architectures
Market-Based Task Allocation

Final Thought

A2A is not about making agents talk.

It is about building distributed systems where reasoning itself is a service.

If microservices changed how we scale code, A2A will change how we scale intelligence.

1. What Is Agent-to-Agent (A2A)?#

2. Why A2A Exists (The Problem It Solves)#

3. Core A2A Concepts (Non-Negotiable)#

3.1 Agents#

3.2 Agent Card#

3.3 Tasks#

3.4 Streaming & Progress Updates#

4. Reference Architecture#

5. Choosing a Tech Stack#

5.1 Languages#

5.2 Transport#

5.3 LLM Integration#

6. Building Your First A2A Agent (Python)#

6.1 Minimal HTTP Server#

6.2 Publishing the Agent Card#

7. Orchestrator Agent (Coordinator Pattern)#

8. Security (Production-Critical)#

8.1 Transport Security#

8.2 Authentication#

8.3 Authorization#

9. Observability & Reliability#

9.1 Logging#

9.2 Metrics#

9.3 Tracing#

10. Testing A2A Systems#

10.1 Unit Testing#

10.2 Integration Testing#

10.3 Chaos Testing#

11. Deployment#

11.1 Containerization#

11.2 Orchestration#

11.3 Scaling#

12. Common Anti-Patterns#

13. Production Maturity Checklist#

14. Recommended Learning Path#

15. Resources & Further Reading#

Protocol & Specs#

Frameworks#

Cloud & Enterprise#

Research#

Final Thought#