How to Build an AI Agent on Vertex AI Agent Engine

Introduction

AI-powered agents are transforming industries — from customer service chatbots to virtual assistants that automate workflows. With Google Cloud’s Vertex AI Agent Engine, developers can now build, deploy, and scale intelligent agents using state-of-the-art large language models (LLMs) and generative AI.

In this blog, we’ll walk through:
✅ What is Vertex AI Agent Engine?
✅ Key components of an AI agent
✅ Step-by-step guide to building your first agent
✅ Best practices for deployment & optimization

Let’s dive in!

What is Vertex AI Agent Engine?

Vertex AI Agent Engine is a fully managed platform for creating conversational AI agents that understand natural language, retrieve relevant information, and execute tasks. It leverages Google’s Gemini models and supports:

Key Features of Vertex AI Agent Engine

🔹 Fully Managed & Secure
Launch and scale agents using a fully managed runtime, backed by Google Cloud’s robust security — including VPC-SC compliance, IAM integration, and end-to-end lifecycle management. The platform also supports CRUD operations and built-in tracing via Cloud Trace and OpenTelemetry.

🔹 High Quality & Evaluation Built-In
Ensure your agents behave as intended using the integrated Gen AI Evaluation service. Benchmark and monitor outputs to maintain reliability in production.

🔹 Simplified Agent Development
No need to worry about low-level application server setup, auth configs, or IAM permissions. Focus on what matters — your agent’s behavior, tools, prompts, and model parameters. Easily enable features like function calling and access any model available in Vertex AI.

🔹 Framework-Agnostic & Extensible
Bring your own agent! Agent Engine supports agents built with popular Python frameworks including:

• Agent Development Kit (ADK)
• LangGraph
• LangChain
• AG2
• LlamaIndex

You can either adapt existing agents to run on Agent Engine using the SDK’s custom template or start from scratch using a prebuilt framework-specific template.

✅ Why Use Vertex AI Agent Engine?

Whether you’re building a customer support assistant, a multi-tool developer agent, or a decision-making engine that calls APIs, Vertex AI Agent Engine provides the foundation to scale confidently, iterate faster, and deliver production-grade AI experiences.

To get started, check out how to deploy your first agent →

Building Your First Agent on Vertex AI Agent Engine

🧱 Prerequisites

• A Google Cloud project with Vertex AI and Agent Engine APIs enabled.
• IAM permissions: Vertex AI User, Service Account User, and access to the project.
• Python 3.9+ and gcloud CLI installed.

Step 1: Set Up Your Environment

gcloud auth login
gcloud config set project [YOUR_PROJECT_ID]
gcloud services enable aiplatform.googleapis.com
gcloud services enable generativelanguage.googleapis.com
gcloud services enable vertexai.googleapis.com
gcloud services enable agent.googleapis.com

Install the Vertex AI SDK:

pip install google-cloud-aiplatform

Step 2: Create an Agent

Use a template or build from scratch. Templates include LangChain, ADK, or LangGraph.

Example (Python SDK):

from vertexai.preview.generative_agents import Agent

agent = Agent.create(
    display_name="support-bot",
    description="An agent that answers product support queries",
    large_language_model="gemini-1.5-pro-preview-0409",
)

Step 3: Add Tools (Optional)

Agents can call APIs, access a vector store, or execute functions.

agent.add_tool({
    "function_name": "get_order_status",
    "function_spec": {
        "parameters": {
            "type": "object",
            "properties": {
                "order_id": {"type": "string"}
            }
        }
    },
    "handler": get_order_status_handler,
})

Step 4: Deploy the Agent

agent.deploy(endpoint_name="support-agent-endpoint")

You can also deploy via YAML or Vertex AI Studio.

Step 5: Test the Agent

Use Vertex AI Studio, REST API, or SDK:

response = agent.chat("Where is my order #12345?")
print(response.text)

Best Practices for Deployment & Optimization

✅ 1. Modular Design

• Use a tool-based architecture where logic is separated into callable tools/functions.
• Store configurations (e.g., model version, temperature) in a single location.

✅ 2. Model Selection

• Choose the right model for the job: gemini-1.5-pro for reasoning, text-bison for lightweight tasks.
• Test with multiple prompts and compare latency vs accuracy trade-offs.

✅ 3. Prompt Engineering

• Use structured, templated prompts with clear role/task separation.
• Use few-shot examples for better responses in complex domains.

✅ 4. Evaluation Frameworks

• Use Gen AI Evaluation for automated quality benchmarking.
• Validate outputs with real-world examples and human-in-the-loop review where possible.

✅ 5. Observability

• Integrate Cloud Logging and Cloud Trace for real-time monitoring.
• Use latency, token usage, and response rate metrics for optimization.

✅ 6. Security and Access Control

• Use VPC-SC for access boundaries.
• Apply IAM policies to restrict who can invoke or deploy agents.

Conclusion

Vertex AI Agent Engine simplifies building enterprise-grade AI agents with minimal coding. By combining LLMs, knowledge grounding, and APIs, you can create agents that enhance customer experiences and automate workflows.

Reference Documents URL:

Vertex AI Agent Engine overview | Generative AI on Vertex AI | Google Cloud

Quickstart: Develop and deploy agents on Vertex AI Agent Engine | Generative AI on Vertex AI |…

Agent Development Kit

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