How to Build an AI Agent with Claude Code and Vertex AI

Why Building AI Agents in 2026 Is a Game-Changer for Professionals

The landscape of software development has shifted dramatically. In 2026, knowing how to build and deploy intelligent AI agents is no longer a luxury reserved for machine learning engineers at big tech companies — it's becoming a core skill for any professional who wants to stay relevant. And when you combine the power of Claude Code Vertex AI agent architecture with modern VibeCoding methodologies, you get something genuinely transformative.

This guide walks you through the process of building a functional AI agent using Claude Code as your reasoning engine and Google Cloud's Vertex AI as your deployment and orchestration platform. Whether you're a startup founder, a freelance developer, or a business analyst who wants to automate complex workflows, you'll find practical, actionable steps here.

Let's get into it — no fluff, no theory for theory's sake.

Understanding the Core Components: Claude Code and Vertex AI

Before writing a single line of code, you need to understand what you're working with. These two technologies serve different but complementary roles in your agent architecture.

What Is Claude Code?

Claude Code is Anthropic's agentic coding interface, designed to operate directly in your terminal and interact with your codebase at a deep level. Unlike a simple autocomplete tool, Claude Code can read files, execute commands, run tests, and reason across your entire project context. In 2026, it has evolved into one of the most capable agentic development environments available.

Key characteristics of Claude Code include:

• Deep codebase awareness: It reads and understands your entire project structure, not just the file you have open.
• Tool use capabilities: It can execute bash commands, run tests, call APIs, and interact with external services autonomously.
• Long-context reasoning: It maintains coherent reasoning across extended tasks that span multiple files and steps.
• Iterative problem solving: It doesn't just generate code — it tests, debugs, and refines until the task is complete.

What Is Vertex AI?

Vertex AI is Google Cloud's unified machine learning platform. In 2026, it serves as both a model deployment layer and an orchestration environment for complex AI workflows. For our purposes, Vertex AI provides:

• Managed model endpoints: Deploy your agents as scalable APIs without managing infrastructure.
• Agent Builder: A visual and programmatic environment for defining agent tools, memory, and reasoning loops.
• Integration with Google services: Native connections to BigQuery, Cloud Storage, and Google Search grounding.
• Enterprise-grade security: VPC controls, audit logs, and IAM permissions baked in from day one.

When you connect Claude Code's reasoning capabilities with Vertex AI's infrastructure, you get a Claude Code Vertex AI agent that is both intelligent and production-ready.

Setting Up Your Development Environment

Prerequisites You Need Before Starting

Let's be practical. Before you write any code, make sure you have the following in place:

• A Google Cloud account with Vertex AI API enabled
• Node.js 20+ or Python 3.11+ installed locally
• The Google Cloud CLI (gcloud) installed and authenticated
• Claude Code installed via npm install -g @anthropic-ai/claude-code
• An Anthropic API key with access to the Claude 3.5 Sonnet or Claude 3.7 models
• A Google Cloud project ID with billing enabled

Installing and Configuring Claude Code

Once you have Node.js installed, getting Claude Code running is straightforward. Open your terminal and run:

npm install -g @anthropic-ai/claude-code

Then authenticate with your Anthropic API key:

export ANTHROPIC_API_KEY=your_key_here

Navigate to your project directory and simply type claude to start an interactive session. What you'll notice immediately is that Claude Code doesn't just answer questions — it acts. It reads your directory structure, proposes plans, and executes them step by step with your approval.

Enabling Vertex AI on Google Cloud

In your Google Cloud Console, navigate to the API Library and enable the following services:

• aiplatform.googleapis.com — Core Vertex AI API
• storage.googleapis.com — For storing agent artifacts
• cloudbuild.googleapis.com — For CI/CD pipelines
• run.googleapis.com — For deploying your agent as a Cloud Run service

Then set your project and region defaults:

gcloud config set project YOUR_PROJECT_ID
gcloud config set ai/region us-central1

Designing Your AI Agent Architecture

Choosing the Right Agent Pattern

Not all agents are built the same way. In 2026, the most common patterns for production agents are:

• ReAct agents: Reasoning and Acting in a loop — the agent thinks about what to do, does it, observes the result, and thinks again. Best for research and data gathering tasks.
• Plan-and-Execute agents: The agent creates a full plan upfront, then executes each step. Better for deterministic, multi-step workflows.
• Tool-calling agents: The agent has access to a defined set of tools (APIs, functions, databases) and decides which to call based on the user's request. Most practical for business automation.

For a Claude Code Vertex AI agent that serves business use cases — say, automating customer onboarding or generating structured reports from unstructured data — a tool-calling architecture deployed on Vertex AI Agent Builder is usually the right choice.

Defining Your Agent's Tools

In Vertex AI Agent Builder, tools are defined as OpenAPI schemas or as direct Cloud Function integrations. Here's an example of a simple tool definition in Python:

tool_definition = {
  "name": "get_customer_data",
  "description": "Retrieves customer data from CRM by customer ID",
  "parameters": {
    "type": "object",
    "properties": {
      "customer_id": {"type": "string", "description": "The unique customer identifier"}
    },
    "required": ["customer_id"]
  }
}

You define each tool, connect it to a real backend function or API, and then let the agent decide when and how to use it based on the conversation context.

Building the Agent: Step-by-Step with VibeCoding

This is where the VibeCoding methodology really shines. VibeCoding, the approach championed by educators like Óscar de la Torre, is about moving fluidly between AI-assisted code generation and human judgment — using tools like Claude Code to dramatically accelerate development while keeping the developer firmly in control of architecture and quality.

"In 2026, the best developers aren't the ones who type the fastest — they're the ones who think the clearest and know how to collaborate with AI to build things that actually work at scale." — Óscar de la Torre, VibeCoding instructor

Step 1: Scaffold the Project with Claude Code

Open your terminal in an empty project folder and run claude. Then describe what you want to build in plain language:

"Create a Python project structure for a Vertex AI agent that can answer questions about our product catalog. It should have a tools directory, a main agent file, and a configuration file for Vertex AI settings."

Claude Code will generate the entire file structure, write boilerplate code, and even suggest the dependencies you need in your requirements.txt. This alone saves hours of setup time.

Step 2: Implement the Vertex AI Agent Runtime

With your project scaffolded, the next step is implementing the actual Vertex AI connection. Using the google-cloud-aiplatform Python SDK, your main agent file will look something like this:

from vertexai.preview.generative_models import GenerativeModel, Tool
import vertexai

vertexai.init(project="YOUR_PROJECT_ID", location="us-central1")

model = GenerativeModel(
  "gemini-2.0-flash-001",
  tools=[your_tool_list]
)

Wait — Gemini? Yes. Here's an important architectural nuance: Vertex AI's native model interface uses Gemini models for the agent runtime itself. Claude, via Anthropic's API, can be integrated as an external tool or as the primary reasoner via a custom integration. In many advanced architectures in 2026, teams use Claude Code to build and maintain the agent codebase while deploying a hybrid agent that leverages both Claude's reasoning and Vertex AI's infrastructure.

Step 3: Connect Claude as the Primary Reasoner

If you want Claude to be the brain of your Vertex AI-deployed agent, you can route requests through Anthropic's Messages API while hosting the application on Cloud Run via Vertex AI. Your orchestration layer handles the tool calls, state management, and response formatting, while Claude handles the actual reasoning.

This hybrid architecture gives you:

• Claude's superior reasoning for complex, nuanced tasks
• Vertex AI's managed infrastructure for scalability and enterprise compliance
• Google Cloud's native integrations for data access (BigQuery, GCS, etc.)
• Full observability through Cloud Logging and Monitoring

Step 4: Deploy to Cloud Run via Vertex AI

Once your agent logic is working locally, deploying to production using Google Cloud is straightforward. First, containerize your application:

gcloud builds submit --tag gcr.io/YOUR_PROJECT_ID/ai-agent

Then deploy to Cloud Run:

gcloud run deploy ai-agent \
  --image gcr.io/YOUR_PROJECT_ID/ai-agent \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

Your Claude Code Vertex AI agent is now live, scalable, and accessible via a public HTTPS endpoint.

Practical Benefits for Businesses and Professionals in 2026

Why does any of this matter for your business? Here are the concrete advantages of this stack:

• Faster time to value: Using VibeCoding techniques and Claude Code, a skilled developer can go from idea to deployed agent in a single day — not weeks.
• Enterprise scalability: Vertex AI handles autoscaling, so your agent can serve 10 users or 10,000 without architecture changes.
• Cost efficiency: Pay-per-use pricing on Cloud Run means you're not paying for idle server time.
• Compliance and security: Google Cloud's compliance certifications (SOC 2, ISO 27001, HIPAA-eligible) make this suitable for regulated industries.
• Maintainability: Because the codebase was built with Claude Code assistance using clean patterns, your team can understand, extend, and debug it without needing the original developer.
• Competitive advantage: Businesses that deploy intelligent agents in 2026 are automating tasks that competitors still do manually — customer support triage, document analysis, lead qualification, internal knowledge retrieval.

Common Mistakes to Avoid

Not Defining Tool Boundaries Clearly

One of the most common failure modes in agent development is giving the agent tools that are too broad or ambiguous. If your query_database tool can do anything, the agent won't know when to use it — and when it does use it, it may send malformed queries. Be specific. Each tool should do exactly one thing, described in clear, unambiguous language.

Skipping the Evaluation Phase

Agents in production fail in ways that unit tests don't catch. You need an evaluation suite — a set of real scenarios with expected outputs — that you run before every deployment. This is non-negotiable in 2026 if you're building agents that interact with customers or handle sensitive data.

Ignoring Latency Budgets

If your agent makes three sequential API calls and each one takes two seconds, your user is waiting six seconds minimum. Profile your agent's tool call patterns early and parallelize where possible. Claude's tool use capabilities support parallel function calling, and you should use it.

Where to Learn More: Escuela de VibeCoding

If this guide has sparked your interest in building production-ready AI agents using modern tools and the VibeCoding approach, there's a dedicated learning community built exactly for this. The Escuela de VibeCoding, founded by Óscar de la Torre in Madrid, offers structured courses, live sessions, and a community of professionals who are actively building with Claude Code, Vertex AI, and the broader AI development ecosystem in 2026.

You can explore all courses, resources, and community access at escueladevibecoding.com. Whether you're a complete beginner who has never deployed a cloud application or an experienced developer looking to level up with AI-native workflows, the school has learning paths designed for real professional outcomes — not just theoretical knowledge.

The curriculum is updated continuously to reflect what's actually working in production environments right now, which is why so many developers across Spain and Latin America are choosing it as their primary resource for mastering the VibeCoding approach to AI development.

Final Thoughts: The Claude Code Vertex AI Agent Is Your Competitive Edge

In 2026, the developers and businesses that will win are not the ones hoarding information — they're the ones acting on it. Building a Claude Code Vertex AI agent is no longer an advanced research project. It's a practical, achievable goal for any motivated professional with the right guidance and the willingness to learn by doing.

The combination of Claude Code's intelligent, context-aware development capabilities and Vertex AI's enterprise-grade deployment infrastructure gives you a stack that is both powerful and production-ready. Add the VibeCoding methodology to move fast without sacrificing quality, and you have everything you need to build AI agents that genuinely create value for your users and your business.

Start small. Pick one workflow to automate. Build the agent, deploy it, and learn from how it performs in