In a world where thousands of developers are building specialized AI agents, we face a critical challenge that mirrors the early days of the web: a lack of standardization. If every agent exposes its capabilities through a custom API with unique endpoints (/execute, /invokeTask, /process), we create a digital "Tower of Babel." This forces an "N x M" integration nightmare, where every one of N consuming agents must write custom, brittle code to interact with M different agent providers.
This chaos stifles innovation. It makes it impossible to build universal tools, create reliable multi-agent systems, or foster an open ecosystem where agents can seamlessly discover and collaborate with one another. The core problem is the absence of a universal, machine-readable standard for how agents should declare their capabilities and execute tasks.
The Agent-to-Agent (A2A) protocol solves this by standardizing two fundamental aspects of agent interaction: Discovery and Execution.
Discovery via the Agent Manifest (/.well-known/agent.json)
Inspired by web standards like robots.txt, A2A mandates that every compliant agent publish a manifest at a predictable URL. This agent.json file is a machine-readable contract, based on specifications like OpenAPI, that describes the agent's identity and skills. Critically, it includes a strict JSON Schema for the expected inputs and outputs of each skill, allowing a consumer to dynamically learn how to call the agent correctly without any prior hard-coding.
Execution via the Standardized /run Endpoint
Once an agent's skills are discovered, the A2A protocol eliminates ambiguity by mandating a single, universal endpoint for all task execution: /run. The payload sent to this endpoint is a lightweight, standardized JSON-RPC 2.0 request, providing a simple and transport-agnostic way to call a method on a remote agent.
This two-pillar design decouples discovery from execution, creating a clean, predictable, and highly scalable interaction model.
+-----------+ 1. Discovery +-----------------+
| Client |----------------->| GET /.well-known|
| (Manager) | | /agent.json |
+-----------+ 2. Execution +-----------------+
|----------------->| POST /run |
+-----------------+
The power of this standard lies in its simplicity and the developer experience it enables, especially when using a framework like google.adk.
Snippet 1: A Sample agent.json Manifest
A ManagerAgent would first fetch this file to understand how to interact with the ImageResizeAgent.
json
{
"name": "ImageResizeAgent",
"version": "1.2.0",
"provider": "Graphics, Inc.",
"skills": {
"resize": {
"description": "Resizes a source image to specified dimensions.",
"endpoint": "/run",
"protocol": "A2A_JSON-RPC_v1",
"input_schema": {
"type": "object",
"properties": {
"source_url": { "type": "string", "format": "uri" },
"width": { "type": "integer", "maximum": 4096 },
"height": { "type": "integer", "maximum": 4096 }
},
"required": ["source_url", "width", "height"]
},
"output_schema": {
"type": "object",
"properties": {
"status": { "type": "string", "enum": ["success", "error"] },
"processed_url": { "type": "string", "format": "uri" }
}
}
}
}
}
Snippet 2: The A2A /run Request Payload
To execute the resize skill, the client constructs and sends the following JSON-RPC 2.0 payload to the agent's /run endpoint.
json
{
"jsonrpc": "2.0",
"method": "resize",
"params": {
"source_url": "https://example.com/images/large.jpg",
"width": 800,
"height": 600
},
"id": "request-uuid-12345"
}
Snippet 3: Conceptual Server-Side Routing with google.adk
The ADK framework abstracts away the protocol's boilerplate. The developer simply implements the core logic as a Python method.
```python
from google.adk import agents, a2a from flask import Flask
class ImageResizeAgent(agents.SpecialistAgent): """Implements image resizing logic.""" def resize(self, source_url: str, width: int, height: int) -> dict: # ... sophisticated image processing logic ... # ... returns a URL to the resized image in cloud storage ... processed_url = f"https://storage.cloud.google.com/resized/{id}.jpg" return {"status": "success", "processed_url": processed_url}
agent = ImageResizeAgent() app = a2a.App(agent) # The app exposes /.well-known/agent.json and /run
```
Performance: While A2A is transport-agnostic, the choice of transport for the /run endpoint has significant performance implications. For high-throughput, low-latency calls between agents in the same data center, a binary protocol like gRPC is often preferred. For calls originating from web browsers or for simpler use cases, HTTPS is a robust and universally supported choice.
Security: A standardized endpoint requires standardized security.
1. Authentication: The /run endpoint MUST be protected. Every request should be authenticated, typically via a Bearer token in the Authorization header. An A2A Gateway should validate this token before forwarding the request to the agent.
2. Input Validation: The input_schema in the manifest is a powerful, proactive security tool. The ADK server can use this schema to automatically validate the structure and data types of every incoming request payload. Any request to /run with malformed data is immediately rejected with a 400 Bad Request, neutralizing a vast class of potential injection and denial-of-service attacks before they ever reach the agent's core logic.
Standardizing on the A2A protocol and its /run endpoint is the engineering foundation for a mature, scalable agent ecosystem. It directly solves the "N x M" integration problem, delivering a powerful return on investment.
The A2A protocol's simple, two-pillar design provides the stability and predictability required to move beyond bespoke integrations and build a truly cross-platform, interoperable future for artificial intelligence.