Vertex AI Agent Engine: A2A Protocol Tutorial

Vertex AI Agent Engine: A2A Protocol Tutorial

Source: agents/agent_engine/tutorial_a2a_on_agent_engine.ipynb Repository: GoogleCloudPlatform/generative-ai URL: https://github.com/GoogleCloudPlatform/generative-ai/blob/main/agents/agent_engine/tutorial_a2a_on_agent_engine.ipynb

Overview

This Jupyter notebook demonstrates building, deploying, and interacting with Agent-to-Agent (A2A) protocol agents on Google Cloud's Vertex AI Agent Engine, a fully-managed serverless platform.

Key Concepts

A2A Protocol

An open standard enabling AI agents to communicate and collaborate by standardizing capability discovery through Agent Cards and standardized interactions, eliminating custom integrations.

Agent Engine

A fully-managed, serverless platform handling infrastructure, scaling, security, and monitoring for A2A agents.

Setup Requirements

The tutorial requires installing:

• a2a-sdk>=0.3.4 - Open-source SDK for building A2A-compliant agents
• google-cloud-aiplatform[agent_engines, adk]>=1.112.0 - Vertex AI SDK with Agent Engine templates

Authentication via Google Colab is provided, along with Google Cloud project configuration and bucket setup.

Implementation Components

Agent Creation

Uses the Agent Development Kit (ADK) to build an LlmAgent with Gemini 2.5 Flash model and Google Search integration.

Agent Card Definition

Defines agent metadata including:

• Name
• Description
• Skills with examples
• Input/output modes for capability discovery

Agent Executor

Implements the AgentExecutor class bridging A2A protocol with internal agent logic, managing task lifecycle:

• submitted → working → completed

Query Methods

The notebook demonstrates three approaches to interact with deployed agents:

1. Vertex AI SDK - Python SDK for direct management
2. A2A Client - Standard open-source protocol client
3. HTTP Requests - Direct REST API calls using standard endpoints

Local Testing & Deployment

Local Testing

Before cloud deployment, the tutorial validates agents locally using mock requests.

Deployment

Deployment occurs via single client.agent_engines.create() call, which handles:

• Serialization
• Dependency inspection
• Packaging
• Endpoint provisioning

Sample Implementation

The Q&A agent demonstrates practical usage by:

• Answering questions using web search
• Extracting responses into artifacts
• Managing task states throughout execution

Key Takeaways

1. A2A Protocol standardizes agent communication
2. Agent Engine provides fully-managed serverless infrastructure
3. Three interaction methods support different use cases (SDK, A2A Client, HTTP)
4. Local testing validates before cloud deployment
5. Single-command deployment simplifies production release

Related Plugins

This tutorial is relevant to:

• jeremy-vertex-engine - Agent Engine inspection and orchestration
• jeremy-adk-orchestrator - ADK supervisory orchestration with A2A protocol support
• jeremy-vertex-validator - Production readiness validation for Agent Engine deployments
• jeremy-gcp-starter-examples - GCP starter kit examples aggregator

Status: Pending full notebook retrieval Next Steps: Download complete notebook with code cells for detailed implementation examples