Getting Started with AI Agents: A Beginner’s Guide
AI Agents on Google Cloud: Your First Steps to learn
1. What is an AI Agent?
Definition: An AI Agent is like a smart software “robot” that can perceive its surroundings (e.g., read text, receive data), think about what it perceives (reason, learn), decide what to do, and then take action to achieve a specific goal. It’s autonomous, meaning it can operate on its own without constant human babysitting.
- GCP View: On Google Cloud, an AI Agent often means leveraging powerful Large Language Models (LLMs) like Gemini (from Vertex AI) as the “brain,” and using services like Cloud Run or Cloud Functions to host the agent’s logic.
2. Multi-Agent System (MAS)
- Definition: Imagine multiple AI agents working together, like a team! A Multi-Agent System (MAS) is a network of these autonomous agents that interact to solve big, complex problems that a single agent couldn’t handle alone. They can cooperate, coordinate, or even compete.
- GCP View: For MAS on Google Cloud, a crucial component for agents to “talk” to each other is Google Cloud Pub/Sub. This is a highly scalable messaging service where agents can send and receive messages, acting as the “nervous system” for your multi-agent team. Each agent could be deployed as a separate Cloud Run service.
3. ADK (Agent Development Kit)
- Definition: The ADK is a toolkit designed specifically to help you build and deploy AI agents. Think of it as a set of LEGO bricks and instructions optimized for creating these intelligent systems. Google’s ADK simplifies the process of integrating LLMs, defining tools, and managing agent behavior.
- GCP View: Google’s own Agent Development Kit (ADK) is a powerful, open-source Python (and Java) library built specifically to make it easy to develop agents that leverage Vertex AI (especially Gemini models) and are ready for deployment on GCP. It’s the recommended way to get started with agents in the Google Cloud ecosystem.
4. A2A (Agent2Agent Protocol)
- Definition: A2A is an open standard for how AI agents communicate with each other. Just like humans use languages to talk, agents need a standardized way to exchange information and understand each other’s “speech acts” (e.g., “request,” “inform,” “propose”).
- GCP View: While A2A defines the content and intent of agent messages, the delivery mechanism on Google Cloud is typically Pub/Sub. Agents format their A2A messages (often as JSON payloads) and send them via Pub/Sub topics to be received by other agents.
5. MCP (Model Context Protocol)
- Definition: MCP is an open standard that helps AI applications (like agents) connect with external resources. This means standardized ways for an agent to use tools (like searching the web, calling an API, or accessing a database) and retrieve relevant data (context) from outside its core model.
- GCP View: When your AI agent (built with ADK) needs to perform a web search, access a database like BigQuery, or trigger a Cloud Function, MCP principles help standardize how these “tool calls” are structured and executed, making integration seamless within Google Cloud services.
6. Agent Engine (Vertex AI Agent Engine)
- Definition: Once you’ve built your AI agent, you need a place to run it reliably and at scale. Agent Engine is a managed service that handles all the heavy lifting: infrastructure, scaling, security, and monitoring.
- GCP View: Vertex AI Agent Engine is Google Cloud’s dedicated, fully managed platform for running your AI agents in production. It seamlessly integrates with agents built using the ADK and leverages Vertex AI’s powerful capabilities (Gemini models, evaluation, monitoring). It’s where your agents go from a development script to a robust, enterprise-grade application.
Basic Example: Your First Google AI Agent (with Gemini & Python)
Let’s build a super simple AI Agent that can answer questions using the power of Google’s Gemini model, all set up for Google Cloud!
Goal: Create an AI Agent that can answer general knowledge questions by leveraging the Gemini 2.5 Flash model.
What you’ll need:
- Google Cloud Project: (e.g.,
my-ai-agent-project-123). - Billing Enabled: For Vertex AI access.
- Vertex AI API Enabled in your project.
- Python 3.9+ installed.
gcloud CLIinstalled and authenticated (gcloud auth application-default login).- Virtual Environment: Highly recommended!
Steps:
1. Setup Your Environment:
# Create a project folder
mkdir first-google-ai-agent
cd first-google-ai-agent# Create and activate a Python virtual environment
python -m venv .venv
source .venv/bin/activate # On macOS/Linux
# .venv\Scripts\activate.bat # On Windows CMD# Install the Google ADK
pip install google-adk# Set environment variables for your GCP project and region
export GOOGLE_CLOUD_PROJECT="YOUR_GCP_PROJECT_ID" # <--- REPLACE THIS!
export GOOGLE_CLOUD_LOCATION="us-central1" # Or your preferred region like asia-southeast1
export GOOGLE_GENAI_USE_VERTEXAI="True" # Important: Tells ADK to use Vertex AI for Gemini
Remember to replace YOUR_GCP_PROJECT_ID with your actual Google Cloud Project ID.
2. Create Your Agent’s Code (my_agent.py):
Create a file named my_agent.py in your first-google-ai-agent directory:
Python
# my_agent.py
from google.adk.agents import Agent# Define your first AI Agent!
# - name: A unique name for your agent.
# - model: We specify 'gemini-2.5-flash' from Vertex AI.
# - instruction: This is the agent's "personality" or primary goal.
root_agent = Agent(
name="GCP_Knowledge_Assistant",
model="gemini-2.5-flash", # Using Gemini 1.5 Flash for speed
instruction="You are a helpful and informative assistant that knows a lot about general topics. Provide concise and accurate answers."
)# That's it for a basic agent! No tools yet, just direct LLM interaction.
3. Run and Test Your Agent:
The ADK comes with a local development UI that makes testing easy.
From your first-google-ai-agent directory (with the virtual environment active):
Bash
adk run .This command will:
- Start a local web server (usually at
http://localhost:8080). - Open your web browser to the ADK’s interactive UI.
Reference URL to explore more :
https://codelabs.developers.google.com/instavibe-adk-multi-agents/instructions?hl=en#5
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