Agent-to-Agent (A2A) Protocol: Architecture, Standards, and AI Agent Interaction Practices

Let’s be honest. We’ve stopped being surprised that neural networks can write code or generate images. The real headache started afterward. When you need to make these neural networks work together. Imagine: you have a great sales agent, another one for analytics, and a third running on a local server. And they don’t see each other. Like blind kittens.

“Without a single protocol, agents work in isolation. It’s like building a house without blueprints: you have walls, but no roof.” — Founder of ASCN.AI.

I know how frustrating this is. You write one integration, then another, and within a month you have a “zoo” of scripts that you’re afraid to touch. Agent-to-Agent Protocol (A2A) is an attempt to bring order to this chaos. It’s not just another standard. It’s the universal connector we’ve all been missing. So that LangChain can understand AutoGen, and a cloud service can quietly call a local model.

What is the Agent-to-Agent (A2A) protocol and why is it needed?

Put simply: it’s the language agents use to negotiate business. Previously, each lived in its own sandbox. Wanted to link two services? Write a workaround, configure the API, and pray nothing breaks during an update.

A2A removes this barrier. It creates a common transport layer. And, honestly, it changes everything.

Why is this important for business? Money. Isolation is expensive. Companies spent years building custom integrations and burning budgets on engineers. Point-to-point connections are slow and tedious. An open standard cuts these costs by 70–80% (Deloitte Global AI Survey 2025 data). Why reinvent the wheel when there are ready-made wheels?

Technically, Agent-to-Agent protocol is a JSON-RPC-based specification. Sounds dry, but the essence is simple: it describes how agents find each other, check permissions, pass tasks, and return results. The format is strict. AI interoperability issues (compatibility problems) are becoming a thing of the past.

You connect an agent to your CRM. Then to email. Then to analytics. Everyone speaks the same dialect. Implementation moves from “let’s play with technology” to real operations. Scaling becomes a matter of hours, not quarters.

The operating principle is elementary: client-server. The client (requester) sends a request. The server (executor) takes it on and returns the result. With intermediate statuses. You see progress in real time. No “black box.”

Important note on the ecosystem. The protocol is already supported by 50+ tech giants: Atlassian, Box, Cohere, Intuit, LangChain, and others. Plus system integrators like Accenture and Deloitte. Since June 2025, the project has been officially under the auspices of the Linux Foundation. This is a mark of quality you can trust.

Key components of A2A in brief

The Evolution of AI Communication. From FIPA-ACL to Modern A2A

The history of agent protocols began in the 1990s. The first standard was FIPA-ACL. Back then, everything operated on rigid logic: “if event A occurs, perform action B.” KQML added some semantics, but both standards are now hopelessly outdated.

Why? They could not work with probabilistic models. LLMs changed everything.

“The FIPA-ACL specification defined messaging standards for intelligent agents.” — FIPA ACL Specification (2002). https://www.fipa.org/specs/fipa00061/

Comparing FIPA-ACL and LLMs reveals a huge gap. Old protocols did not account for hallucinations, dynamic context, or tokenization. The world has changed over the past two decades, while the documentation remained stagnant.

The evolution of multi-agent systems followed a spiral path. GPT-3 was released in 2020. The first agent frameworks appeared in 2023. Now, in 2024–2025, the market is demanding: “Where is the standard?” Different vendors, different logic — a unified network layer is needed.

The modern Agent-to-Agent protocol is designed for neural networks. JSON for structure. Tokens for context understanding. Streaming for long-running tasks. Without this, every agent is like a peripheral device requiring its own driver. Exhausting.

Key Concepts: Agent, Task, and Artifact

Atomic units of A2A are the foundation. Three concepts hold the entire system together. Without them, there is no magic.

The difference between A2A client and server is critical. The client initiates the process. The server executes it. The same agent can switch roles: today it is the customer, tomorrow the contractor. It depends on the task context.

What is Agent Card? It is a JSON file at a standard address (well-known URL). It says: “Hello, I am an agent, I can do this, I authenticate like this.” A machine-readable business card.

Tasks and artifacts are tightly linked. The task describes “what.” The artifact is the “result.” A file, text, data. Everything is structured.

State management (task state management) tracks progress. Statuses are transparent: submitted → working → completed/failed. The client either polls the server or the server pushes updates. Creating your first ASCN Agent now takes hours, not days. For more details on configuration, see the guide on creating an AI agent.

Practice. In ASCN.AI projects, we used this model for lead generation. One agent collected requests from Telegram. Another qualified them. A third pushed them to the CRM. Each had an Agent Card in the registry. Result: 3,000 leads per month without human involvement.

Technical architecture of the protocol. How it works under the hood

A2A technical specification is based on JSON-RPC 2.0. This means: a clear structure. Method, parameters, ID, response. Any developer familiar with APIs will figure it out in an hour. The documentation is compact, just 15 pages.

“JSON-RPC 2.0 provides lightweight data transport over the network.” — JSON-RPC 2.0 Specification, The Open Group (2010).

The ASCN Agent workflow is clear. Discovery. Authentication. Request. Execution. Return. Each stage is standardized.

The architecture of multi-agent systems is modular. You add a new agent by registering it in the registry. It immediately becomes available to others. Horizontal scaling, without pain.

JSON RPC in AI provides the transport. The prompt defines the semantics, and the protocol defines the structure. Separation of responsibilities. Brilliant in its simplicity.

Diagram. A2A request lifecycle

1. Discovery 2. Authentication 3. Execution 4. Artifact Return

Alt Text: A2A Client and Agent Server interaction diagram via JSON-RPC, showing the Discovery, Authentication, Task Execution, and Artifact Return stages.

Stage 1. Discovery and authentication

How do agents find each other? The discovery mechanism handles this. Agent Card is published at a well-known URL. Usually, this is домен-агента/.well-known/agent-card.json. The client accesses it and receives metadata.

Agent discovery protocol works like DNS, but for agents. You know the domain — find the card — understand the capabilities — connect.

A2A authentication uses standards: OAuth 2.0, API Keys, JWT. It depends on requirements. Public agents are open, corporate ones are behind a lock.

The well-known JSON endpoint follows the convention. This enables automatic discovery. No manual configuration is needed.

In ASCN.AI, discovery is implemented via a central registry. An agent registers and receives an entry. Others find it by tag or name. For internal systems, this is extremely convenient.

Stage 2. Message exchange (Push and Pull models)

Synchronous and asynchronous exchange in A2A covers different scenarios. Quick request — immediate response. Long task — streaming.

Request/Response mode for simple operations. “What’s the weather?” — “+20”. Done. Milliseconds.

Streaming agent response is needed for long-running tasks. PDF analysis, report generation, model fine-tuning. The client receives data in chunks as it becomes ready.

Push notification for AI agents allows the server to wake up the client. Task complete. Error. Approval needed. Without constant polling.

Synchronous is easier to debug. Asynchronous is more reliable in production. The choice depends on the SLA.

Stage 3. Task state management (Task State)

A task goes through 4 statuses: submitted → working → completed/failed. The client tracks progress. You can poll every second. Or set up a webhook for status changes. Webhooks save server resources.

Polling works on an interval. Requested status — received “working” — asked again. Plus intermediate artifacts, if any.

In the ASCN.AI case study on the Falcon Finance crash, we used this model for monitoring. The agent tracked the price. When the trigger fired, it sent a task to the trading bot. Status changed in real time. ASCN.AI case study on the Falcon Finance crash demonstrates the state machine in action. Result: plus $1,000 from 2 prompts during the flash crash.

Comparison of communication standards. A2A vs FIPA vs MCP

Comparison of AI protocols shows a trend toward consolidation. The market is tired of the zoo of standards. A2A has specific advantages for LLM agents.

Table. Protocol comparison

A2A vs MCP — this is a difference in philosophy. MCP (from Anthropic) focuses on access to context and tools. A2A focuses on agents interacting with each other. For multi-agent orchestration, the latter is preferable.

Case from IBM Think: the Inventory agent uses MCP for the database. The Order agent uses A2A to communicate with external suppliers. (each in its place).

FIPA-ACL today is more of a legacy. The standard did not account for probabilistic models. Rigid semantics where LLM flexibility is needed.

Choice of protocol for agents depends on the task. Internal automation — A2A. Integration with legacy APIs — MCP or REST. Fundamental research — FIPA.

Why A2A is better than proprietary APIs and old standards

An open protocol cuts integration costs. JSON is universal. Supported everywhere. No binary formats or vendor lock-in.

Why A2A instead of REST API? REST requires a rigid endpoint structure. A2A allows semantic interpretation. The agent understands the task (“find contacts”), rather than just calling a function. The difference between an executor and a partner.

AI platform compatibility is achieved through a standard. You mix agents from different vendors. LangChain with AutoGen. Local models with cloud ones. The protocol hides implementation complexity.

In the Turnkey Automation project, we connected CRM, email, and analytics via A2A. Each service has its own agent. Data exchange without custom integrations. Implementation time reduced from 6 weeks to 5 days.

Practical implementation. How to start working with A2A

A2A getting started requires understanding the basics. The protocol is simple. JSON-RPC over HTTP. Authentication options available. Standard statuses.

Tools and SDKs

The official repository supports major languages. Install packages:

pip install a2a-sdk
go get github.com/a2aproject/a2a-go
npm install @a2a-js/sdk

Specification documentation is in the public Linux Foundation repo.

For business, there is a No-Code path. The ASCN.AI platform provides a visual builder. Ready-made templates, connectors to 100+ services. No coding required. Configure logic by dragging and dropping blocks.

Automation templates accelerate the start. Choose a framework, implement endpoints, test. Done.

Code example. Creating an Agent Server (Python) with Streaming and Push handling

Code server agent python shows a minimal implementation. FastAPI for HTTP, JSON-RPC for structure. SSE support added for streaming.

import asyncio
import json
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
from sse_starlette.sse import EventSourceResponse

app = FastAPI()

async def stream_result(task_id: str):
    """Генерация промежуточных этапов задачи для клиента."""
    for i in range(3):
        yield f"data: {{\"task_id\": \"{task_id}\", \"status\": \"working\", \"progress\": {i * 33}}}\n\n"
        await asyncio.sleep(1)
    yield f"data: {{\"task_id\": \"{task_id}\", \"status\": \"completed\"}}\n\n"

@app.post("/a2a")
async def handle_task(request: Request):
    body = await request.json()
    task_id = body.get("id", "default_123")
    method = body.get("method", "")
    
    if method == "analyze":
        # Для долгих задач возвращаем SSE-стрим
        return EventSourceResponse(stream_result(task_id))
    else:
        # Для синхронных задач
        return {"id": task_id, "result": {"output": "ready"}, "status": "completed"}

FastAPI example for AI agents demonstrates the principle. The endpoint accepts JSON-RPC. Separates requests. LangChain integration is possible via custom tools. JADE is legacy-compatible, but Python/Go SDKs are better for modern stacks.

Real use cases

Examples of multi-agent systems show where the protocol shines. Single agents are useful, but combinations create synergy.

Business automation with agents is already running in production. Real companies save hours. Not demo videos.

A2A use cases for enterprise: finance, logistics, support. Any routine task.

Autonomous DAOs are the future. Agents vote and execute decisions. Audit on the blockchain.

Corporate automation. Coordination of agent-employees

Scenario: an HR agent delegates a task to an Accounting agent. An employee resigns. The HR agent receives the signal and creates a task. Accounting revokes access, calculates compensation, and updates the registry.

HR automation via AI agents saves up to 80% of time. No manual requests. No missed steps. The protocol guarantees sequence.

At ASCN.AI, we automated leads this way. A lead arrives — the qualification agent evaluates it. Sales reps receive only “warm” leads. Accounting issues invoices automatically. Conversion increased by 34% in a quarter. [ASCN.AI data, 2025]

Decentralized applications (dApps) and DAOs

Using A2A in Web3 opens doors. Agents can own wallets and sign transactions.

AI agents in DAOs operate as participants. Each has an address and keys. Decisions are collective, execution is automatic.

The ASCN.AI case study on the flash crash of October 11 demonstrates the power of autonomous decisions. Case study: earning on the flash crash: an analytical agent monitored the market; upon the drop, it sent a task to the trading agent. The latter shorted and closed on the rebound. Without human involvement.

AI-driven trading operations carry risks. This is not investment advice.

Interaction smart contracts and AI requires oracles. The agent fetches external data and passes it to the contract. The contract executes, and the agent confirms.

Security and challenges of the A2A protocol

Agent-to-agent security is critical for production. Requirements: OAuth 2.0, JWT, token rotation.

Vulnerabilities in multi-agent systems: prompt injections, agent spoofing. The standard helps, but it is not a panacea. More about risks in cryptocurrency.

Trust in AI is built through verification. Signed Agent Cards, reputation, and log audits.

Hallucinations create risks. A server may return nonsense. The client must validate using rules or cross-checking.

Security checklist before launching an autonomous agent

• Check the authentication scheme in the Agent Card (OAuth2/JWT).
• Set up a sandbox for test requests before production.
• Implement cost caps on tools for bots.
• Enable logging of all prompts.
• Conduct penetration testing for prompt injection.

Fact-checking by AI agents can be done via cross-checking. One responds, the other verifies. Trust between AI models is built up gradually. Start with low stakes. Data protection includes encryption and secrets in a secure enclave.

Security information is general. Integration requires an audit tailored to your infrastructure.

Frequently Asked Questions (FAQ)

Is A2A an open standard? A2A open source status is confirmed. GitHub repositories are maintained by the community. Contributions are welcome.

Who develops the protocol? A consortium of technology companies plus independent researchers. The specification evolves through an RFC process managed by the Linux Foundation.

Can A2A be used with local LLMs? Local LLMs and protocols are fully compatible. A2A is a transport layer. The model does not matter. An Ollama agent can act as a server. The endpoint is local, with the card on localhost.

What is the difference between A2A and a regular API call? API vs AI agent difference lies in semantics. An API requires an exact format. An agent interprets intent. You state what needs to be done, not how to do it. REST cannot do this.

Principles of working with distributed systems are described in Blockchain and Cryptocurrency Fundamentals, which partially overlaps with the architecture of multi-agent networks.

Conclusion

The Agent-to-Agent Protocol changes the game. Isolated agents are becoming obsolete. Connected systems are taking their place. You can build automation that scales without rewriting code.

Start small. One agent. One task. The protocol allows you to add new modules as you grow. Without integration pain.

ASCN.AI offers a platform for launching such systems. No-code environment, ready-made integrations. Turnkey Automation will handle the technical side.

The platform supports 100+ scenarios. Sales, marketing, content. Choose, configure, launch. Manage AI agents via a single dashboard.

For complex tasks, turnkey implementation is available. Audit, architecture, training. You get ready-to-use infrastructure.

 The specification is evolving. Follow the repositories. The standard belongs to those who use it.

Ready to launch? Connect your first agent today. Configure interactions. Scale autonomously.

Sources

1. FIPA ACL Specification. Foundation for Intelligent Physical Agents. 2002.
2. JSON-RPC 2.0 Specification. The Open Group. 2010.
3. Google A2A Protocol Announcement. Google Cloud. April 9, 2025.
4. Agent2Agent (A2A) Protocol Repository. Linux Foundation. June 2025.
5. Model Context Protocol Documentation. Anthropic. 2024.
6. Deloitte Global AI Survey 2025. Enterprise Integration Costs.