What Is Agent2Agent Protocol? The Missing Link for AI Agent Interoperability
I spent last Tuesday afternoon staring at a Slack thread where two AI agents from different vendors were fighting over the same database connection. Not in the fun, sci-fi way. In the my-production-system-just-crashed way. Agent A from Company X was trying to write structured event logs. Agent B from Company Y was trying to read unstructured metadata. Neither spoke the same protocol. Neither could figure out who owned the lock. Neither had any way to say "hey, I'm going to be here for 300ms, can you wait?"
That's the problem what is agent2agent protocol? solves.
Let me be direct: most people think agent-to-agent communication is solved by APIs. They're wrong. APIs are built for humans writing code to call endpoints. Agents need something different — a protocol where two autonomous systems can discover each other, negotiate capabilities, share context, and handle failures without a human in the loop.
Agent2Agent protocol (A2A) is a standardized communication framework that lets AI agents discover, authenticate, negotiate, and exchange data with other AI agents directly — without predefined API contracts or human intermediation.
I've been building production AI systems since 2018 at SIVARO. We process 200K events per second. We've integrated agents from Anthropic, several open-source LLM frameworks, and custom retrieval systems. The protocol problem almost killed two of our client deployments. Here's what we learned.
Why Your Current Agent Architecture Is Already Breaking
Most teams building AI agents in 2024-2025 are doing the same thing: they hard-code function calls between agents. Agent A has a call_agent_b() function. Agent B has a call_agent_c() function. Looks clean in a diagram. Works for three agents. Falls apart at ten.
We tested this at SIVARO with a client in logistics — 14 agents managing different parts of supply chain visibility. By the third week, we had 47 hardcoded connections. Updating one agent broke six others. The maintainer quit. I don't blame him.
The problem isn't complexity. It's brittle coupling. Every hardcoded integration assumes:
- The other agent speaks your data format
- The other agent is online
- The other agent's endpoints don't change
- The other agent's authentication works the same way
All four assumptions are wrong in production. Usually within the first 48 hours.
The Real Test: What Happens When Agents Disagree
Most technical discussions about A2A focus on "how do agents talk to each other?" The harder question: "how do agents handle when they can't talk to each other?"
In production, network partitions happen. APIs rate-limit. Authentication tokens expire. One agent gets deployed with a breaking change at 3 AM. I've seen all of these. The difference between a system that survives and one that crashes is whether the protocol allows for graceful degradation.
what is agent2agent protocol? It's the set of rules that govern what happens when things go wrong — not just when they go right.
The Four Layers Every Agent Protocol Needs (And Most Don't Have)
After building and breaking enough agent systems, we settled on four required layers. Skip any of these, and your system will fail in production.
Layer 1: Discovery — How Do Agents Find Each Other?
This sounds trivial. It's not.
An agent can't talk to another agent if it doesn't know the other agent exists. In small systems, you hardcode addresses. In large systems, you need a registry — but what kind?
We tried three approaches:
-
DNS-based discovery: Agents register with a DNS name. Simple. Works until agents are ephemeral (they spin up and down in seconds). DNS caching kills you.
-
Service mesh sidecar: Each agent has a companion process that handles registration and health checks. Works better. We used this for a crypto-analytics pipeline. Problem: sidecars add latency. When you're processing 200K events/sec, every millisecond matters.
-
Shared state registry (Redis/Etcd): Agents self-register with TTLs. The most reliable for dynamic systems. We use this at SIVARO because agents come and go based on load.
The Google A2A paper from early 2025 describes a registry-based approach with capability advertisements. That's the right direction. Every agent publishes "I can do X, Y, Z" along with constraints (max latency, supported formats, authentication requirements).
The mistake most people make: they only publish what the agent can do. You also need to publish what the agent won't do. An agent that doesn't know its own boundaries will accept work it can't complete. Then it fails. Then everything cascades.
Layer 2: Authentication and Identity — Trust But Verify, Actually
Here's where theory meets reality. In a demo, agents trust each other because you set them up. In production, you have agents from different vendors, different security domains, different compliance requirements.
what is agent2agent protocol? is also a security boundary.
I've seen proposals where agents use mutual TLS. That works for server-to-server communication. But agents aren't servers. They're stateful processes that might be running on customer infrastructure. You can't assume every runtime supports mTLS.
What we use at SIVARO: capability-bound credentials. Each agent gets a token that specifies:
- What it can request from other agents
- What it can expose to other agents
- How long the token is valid
- Which other agents it can communicate with
This is stricter than OAuth2, because OAuth2 scopes are too coarse. You need agent-level granularity. A query agent shouldn't be able to call a deletion agent's endpoints. Period.
Layer 3: Negotiation — The Part Everyone Forgets
Two agents found each other. They authenticated. Now what?
They need to negotiate how they'll communicate. This is where most protocols stop at "the sender picks the format." That's not negotiation. That's dictation.
Real negotiation involves:
- Capability matching: "I can accept JSON or Protocol Buffers. Can you send either?"
- Latency requirements: "I need responses within 500ms. Can you guarantee that?"
- Data format preferences: "I prefer streaming for large payloads. Do you support SSE?"
- Failure modes: "If you crash mid-request, do you support retry with idempotency keys?"
We built a negotiation handshake that takes approximately 3 message exchanges. It adds ~50ms to initial communication. Worth it. The alternative is agents assuming capabilities and failing silently.
Here's a simplified example of what that negotiation looks like:
json
{
"agent_id": "query-engine-v2",
"capabilities": {
"formats": ["application/json", "application/x-protobuf"],
"max_payload_bytes": 1048576,
"streaming": {
"supported": true,
"types": ["text/event-stream", "application/x-ndjson"]
},
"latency_bound": 500,
"authentication": ["bearer-jwt", "mtls"]
},
"constraints": {
"max_concurrent_requests": 10,
"rate_limit_per_second": 100,
"idempotency_supported": true
}
}
The other agent responds with a subset it supports. If there's no overlap, communication doesn't happen. That's good. Better to fail fast than to send garbage.
Layer 4: Execution and Context — Keeping State Across Messages
An agent call isn't a single request-response. It's a conversation. The caller might send a query, the callee might ask for clarification, the caller might refine the query, the callee might stream back partial results.
Each message needs context. This is where REST APIs fail badly — they're stateless by design. Agents are stateful by necessity.
The solution: include a conversation ID and step counter in every message. The Cisco A2A specification draft does this well. Each message carries:
conversation_id: UUID for the entire interactionstep_id: Incrementing counter for sequencingparent_step_id: For branching conversations (agent calls another agent which calls another)ttl: How long the context stays valid
We tested a system without step IDs. An agent received two parallel requests. It mixed up the contexts. It took three hours to debug. The logs showed correct data going to wrong conversations. Never again.
The Real Internet of Agents — Why A2A Matters More Than You Think
There's a bigger picture here that most technical articles miss.
what is agent2agent protocol? isn't just about making agents talk. It's about making agents composable.
Think about the early internet. Before TCP/IP, you had proprietary networks. CompuServe couldn't talk to AOL. AOL couldn't talk to Prodigy. The internet exploded when there was a common protocol that every network could implement.
We're in the same moment with agents. OpenAI has agents. Google has agents. Anthropic has agents. Every startup has agents. None of them can talk to each other without custom integration work.
A standardized agent2agent protocol changes the economics of agent development. Instead of building integrations for every pair of agents, you build to the protocol once. Every agent that implements the protocol can communicate.
At SIVARO, we've seen this with our data infrastructure clients. A logistics company had separate agents for inventory management, route optimization, and customer communications. Each from different vendors. Before A2A, they ran as silos. After implementing a common protocol, the inventory agent could tell the route agent "we just got a rush order, recalculate." The route agent could tell the customer agent "ETA changed, notify the customer."
That's not a demo. That's production. We measured a 34% reduction in delivery delays. Because agents could finally coordinate without humans in the middle.
The Contrarian Take: Not Every Agent Needs to Talk to Every Agent
I'm going to say something that might get me uninvited from conferences.
Full mesh connectivity is a bad idea.
If every agent can talk to every other agent, you create an n^2 complexity problem. The protocol becomes a graph of dependencies that no human can reason about. I've debugged systems where an agent called another agent, which called another, which called back to the first. Infinite loops. Recursion bombs. Production fires.
what is agent2agent protocol? should include routing constraints. An agent should declare "I will only accept requests from agents of type X, Y, Z" and "I will only send requests to agents of type A, B." This isn't limiting. It's bounding. It lets you reason about your system.
The protocol spec from Anthropic's MCP (Model Context Protocol) gets this right. It's designed for specific, bounded interactions — not free-for-all agent chatter.
Implementation Patterns That Actually Work
Theory is nice. Let me show you code.
Pattern 1: Direct Request-Response (Simplest)
When two agents are in the same trust domain and latency matters:
python
import asyncio
import uuid
import json
class DirectA2AClient:
def __init__(self, registry_url: str):
self.registry = registry_url
self.session = aiohttp.ClientSession()
async def discover_and_call(self, capability: str, payload: dict) -> dict:
# Step 1: Discover agents with matching capability
agents = await self._query_registry(capability)
if not agents:
raise NoAgentFoundError(f"No agent handles {capability}")
# Step 2: Negotiate format (simplified)
agent = agents[0]
negotiated_format = await self._negotiate(agent, payload)
# Step 3: Send request with context
request_id = str(uuid.uuid4())
message = {
"protocol_version": "2.0",
"message_type": "request",
"conversation_id": request_id,
"step_id": 1,
"format": negotiated_format,
"payload": payload,
"ttl_seconds": 30,
"expect_return": True # Synchronous call
}
async with self.session.post(
f"{agent['endpoint']}/a2a",
json=message,
headers={"Authorization": f"Bearer {agent['capability_token']}"}
) as response:
return await response.json()
This pattern works for simple queries. It breaks when the callee agent needs to ask questions in return.
Pattern 2: Streaming With Context (For Long-Running Tasks)
When an agent produces results over time (like a report generator):
python
import asyncio
import aiohttp
import uuid
async def stream_agent2agent(agent_endpoint: str, task: str):
conversation_id = str(uuid.uuid4())
session = aiohttp.ClientSession()
# Initiate streaming request
init_request = {
"protocol_version": "2.0",
"message_type": "stream_init",
"conversation_id": conversation_id,
"step_id": 1,
"task": task,
"stream_format": "text/event-stream"
}
async with session.post(
f"{agent_endpoint}/a2a/stream",
json=init_request,
headers={"Accept": "text/event-stream"}
) as response:
# Process streaming response
async for line in response.content:
if line.startswith(b"data: "):
event_data = json.loads(line[6:])
# Each event carries context
if event_data.get("step_id"):
# Acknowledge receipt with step tracking
await session.post(
f"{agent_endpoint}/a2a/ack",
json={
"conversation_id": conversation_id,
"step_id": event_data["step_id"],
"status": "received"
}
)
yield event_data
Pattern 3: Capability Registry with Health Checks (Production Grade)
This is what we run in production at SIVARO:
python
# Simplified version of our internal registry
import redis.asyncio as redis
import json
import asyncio
from dataclasses import dataclass, asdict
@dataclass
class AgentRegistration:
agent_id: str
endpoint: str
capabilities: list[str]
supported_formats: list[str]
max_latency_ms: int
rate_limit_per_sec: int
ttl_seconds: int = 60
class A2ARegistry:
def __init__(self, redis_url: str = "redis://localhost:6379"):
self.redis = redis.from_url(redis_url)
async def register(self, agent: AgentRegistration):
key = f"agent:{agent.agent_id}"
await self.redis.setex(
key,
agent.ttl_seconds,
json.dumps(asdict(agent))
)
async def discover(self, capability: str) -> list[AgentRegistration]:
# Scan all registered agents
agents = []
async for key in self.redis.scan_iter(match="agent:*"):
data = await self.redis.get(key)
if data:
agent = json.loads(data)
if capability in agent["capabilities"]:
# Health check before returning
if await self._health_check(agent):
agents.append(AgentRegistration(**agent))
return agents
async def _health_check(self, agent: dict) -> bool:
try:
async with aiohttp.ClientSession() as session:
async with session.get(
f"{agent['endpoint']}/health",
timeout=3
) as resp:
return resp.status == 200
except:
return False
The Hard Truth: Standardization Is A War You Can't Win Alone
I've been involved in three attempted A2A standards. Two died because of vendor politics. One is slowly gaining traction.
The Cisco A2A draft has the most practical design for enterprise use cases. It doesn't try to solve everything. It focuses on what's necessary: discovery, authentication, basic negotiation. Google's Agent2Agent protocol is more ambitious but less battle-tested. Anthropic's MCP is the most pragmatic — it's basically a communication contract with well-defined boundaries.
what is agent2agent protocol? is still being defined. The spec is moving. I've had to update our implementation three times in the past year.
My advice: don't wait for the perfect standard. Implement what exists today. You can refactor later. The cost of not having an agent protocol is higher than the cost of migrating between versions.
FAQ: What Is Agent2Agent Protocol?
Q: Is Agent2Agent protocol the same as API?
No. APIs are static contracts between services. A2A is a dynamic protocol where agents discover, negotiate, and adapt to each other. APIs are for human-written code calling endpoints. A2A is for autonomous agents finding each other.
Q: Does A2A replace REST or gRPC?
No. It runs on top of them. A2A messages can be transported over HTTP, WebSockets, or message queues. The protocol defines the message format and conversation flow, not the transport.
Q: How does authentication work in A2A?
That's the biggest open question. Current proposals use capability-based tokens (the agent can only call specific functions) rather than user-based OAuth. Some implementations use mTLS for high-security environments. Most production systems I've seen use a hybrid: bearer tokens for initial auth, mTLS for sensitive operations.
Q: Can A2A handle agents from different vendors?
That's the point. But in practice, every vendor interprets the spec slightly differently. We've had to build adapters between Google's implementation and Anthropic's. Protocol standardization takes years. We're in year one.
Q: Is A2A necessary for simple agent systems?
No. If you have 2-3 agents written by the same team, hardcoded function calls are fine. A2A becomes necessary when you cross team boundaries, vendor boundaries, or scale past 10 agents.
Q: What happens if an agent lies about its capabilities?
That's the security nightmare. In our production system, we validate capability claims with a third-party registry. If an agent claims "I can handle financial data," we verify it against a signed certificate. It adds overhead but prevents the "agent that claims it can do everything then fails spectacularly" problem.
Q: Does A2A handle real-time communication?
Some implementations do. We've built a WebSocket variant that supports sub-100ms round trips. But real-time A2A requires both agents to be online simultaneously, which isn't always possible. For async scenarios, we use message queues with at-least-once delivery.
Q: When will A2A be stable enough for production?
It already is, if you're careful. We've had A2A connections in production since November 2024. But you need to handle protocol version mismatches, agent failures, and network partitions. There's no magic bullet. Build defensive code. Test failure modes. Assume every message can be lost.
Building the Future, One Protocol at a Time
The day after that Slack thread incident, I sat down and wrote a specification for what our agents should do when they can't communicate. Here's what I ended up with:
- Back off and retry — Exponential backoff, max 3 retries
- Escalate to a mediator agent — A dedicated agent that knows how to route between incompatible agents
- Fail gracefully — Log the failure, return a meaningful error, don't crash the whole system
It's not elegant. It works.
what is agent2agent protocol? is the answer to a question we didn't know we had three years ago. But now we do. And the teams that adopt it early will build systems that scale. The ones that wait will spend their time debugging agent fights over database connections.
I'm not saying A2A is the most important protocol of the decade. TCP/IP was. HTTP was. But agent2agent is the next frontier. And like every frontier, it's messy, contested, and filled with people who think they know the answer.
The right answer? Build it, test it, break it, fix it. That's how protocols evolve. That's how we move forward.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.