Vertex AI Agents Builder Tutorial | Build AI Agents

Building AI-powered agents has shifted from research labs to everyday enterprise applications, and Google’s Vertex AI Agents Builder makes it easier than ever. Whether you’re an engineer exploring conversational AI, a data scientist integrating automation, or a startup founder building an AI-driven product, this tutorial will walk you step by step through setting up, training, and deploying intelligent agents on Vertex AI.

What is Vertex AI Agents Builder and how do you use it?‍

Vertex AI Agents Builder is a Google Cloud tool that lets developers create, manage, and deploy AI agents without needing complex infrastructure. Using it, you can design conversational workflows, connect agents to data sources, and scale them for real-world applications. A typical tutorial includes setting up Vertex AI, defining an agent’s skills, integrating APIs, testing interactions, and deploying to production.

Vertex AI Agent Builder Pricing | Complete Analysis

Vertex AI Agent Builder follows a pay-as-you-go model, with costs primarily split across the agent engine runtime, underlying generative AI models, and search/conversational features.

Core Infrastructure Costs

As of late 2025, the Agent Engine Runtime (the environment where your agent lives) is billed based on compute resources used:

• vCPU: Starting December 16, 2025, the rate is $0.0864 per vCPU-hour.
• Memory: Starting December 16, 2025, the rate is $0.0090 per GB-hour.
• Code Execution: Beginning January 28, 2026, agents using code interpreter tools will be billed at the same $0.0864/vCPU-hour and $0.0090/GB-hour rates.

Search and Generative Query Costs

If your agent uses Vertex AI Search for data retrieval or grounding, pricing is based on the volume of queries:

• Standard Search: Starting at $1.50 per 1,000 queries.
• Enterprise Search (Generative Answers): $4.00 per 1,000 queries.
• Conversational Commerce: Conversational queries are billed at $6.00 per 1,000 requests.
• Grounded Generation API: If grounding on your own retrieved data, it costs $2.50 per 1,000 requests in addition to model token costs.

Underlying Model and Data Storage

• Model Usage: Charges for input and output tokens depend on the specific model used, such as Gemini 1.5 Pro or Flash. The rates are in the Vertex AI Model Garden.
• Data Storage: Storing raw data for indexing and search typically costs $1.00 per GB per month for configurable models. Website indexing often depends on the number of pages.
• Sessions & Memory: Starting January 28, 2026, stored session events and "memories" will cost $0.25 per 1,000 events or memories.

Free Tiers and Credits

• Free Monthly Queries: Many search configurations include the first 10,000 queries per month for free.
• Cloud Credits: New customers can access a $300 free credit, valid for 90 days, to test Agent Builder and other Google Cloud services.
• No-Cost Preview: An "Express Mode" allows limited experimentation with up to 10 agent engines for 90 days without enabling billing.

Building AI Agents with Vertex AI Agent Builder

Building AI agents with Vertex AI Agent Builder (formerly Vertex AI Search and Conversation) allows you to create enterprise-ready conversational and search-based systems. The platform is designed to be accessible, offering no-code visual interfaces alongside advanced developer tools for deep customization.

Core Components of Agent Builder

The platform is structured around three primary pillars that support the entire lifecycle of an AI agent:

• Build (Agent Development Kit - ADK): An open-source framework used to design agents with minimal code (often under 100 lines of Python). It provides deterministic guardrails and orchestration controls to manage how agents think and collaborate.
• Scale (Agent Engine): A fully managed runtime for deploying and scaling agents in production. It handles infrastructure, security, and "memory bank" services that allow agents to retain context across user interactions.
• Govern (Security & Compliance): Centralized tools to manage agent identities via Google Cloud IAM, enforce safety filters, and monitor agent actions through full audit trails and tracing.

Step-by-Step Development Workflow

1. Environment Setup: Create a Google Cloud project, enable the Vertex AI and Dialogflow CX APIs, and set up billing.
2. Connect Data (Grounding): Create a Data Store to link your agent to proprietary data, such as PDFs, websites, or BigQuery tables. This process, known as Retrieval Augmented Generation (RAG), ensures the agent's responses are accurate and factual.
3. Design Behavior: Use the Agent Designer (a low-code visual editor) to define the agent's goal and natural-language instructions (e.g., "Greet the user and help them with travel planning based on the uploaded data").
4. Testing and Refinement: Use the built-in simulator (Playground) to test interactions. You can adjust instructions or add more data to improve accuracy.
5. Deployment: Publish the agent to a managed environment. You can integrate it into your own platforms using a provided HTML code snippet (web widget) or by calling the API directly.

Integration and Interoperability

• Multiple specialized agents can connect using the Agent2Agent (A2A) protocol. This allows the agents to work together on complex tasks.
• The Agent Builder is optimized for Google's Gemini models. It also supports third-party and open-source models available in the Vertex AI Model Garden.
• Over 100 pre-built connectors are available to link the agent with enterprise systems. Examples include Salesforce, ServiceNow, and Jira.

What features are available as part of Vertex AI Agent Builder?

Vertex AI Agent Builder is a unified platform for creating, deploying, and managing enterprise-grade AI agents. It integrates features previously known as Vertex AI Search and Conversation into a single, comprehensive ecosystem.

Key available features as of late 2026 include:

Development and Orchestration

• Agent Development Kit (ADK): A framework that allows developers to build production-ready agents and multi-agent workflows using Python or Java (with TypeScript support recently added).
• No-Code Console: An intuitive drag-and-drop interface for users to design, test, and deploy conversational agents using natural language without writing code.
• Multi-Agent Orchestration: Capabilities to stitch multiple specialized agents together into complex workflows, allowing one "main" agent to coordinate tasks among various sub-agents.
• Agent Garden: A centralized library where developers can discover and use pre-built agent samples, tools, and reference architectures to accelerate development.

Deployment and Scaling

• Vertex AI Agent Engine: A fully managed runtime service that handles infrastructure, auto-scaling, and security for deploying agents into production.
• Short and Long-Term Memory: Integrated services (Sessions and Memory Bank) that allow agents to maintain context across multiple user interactions for a more human-like experience.
• Secure Code Execution: A managed, sandboxed environment where agents can safely execute generated code for tasks like complex data analysis or mathematical modeling.

Data and Grounding

• Grounding with Google Search and Maps: Connects agents to live, authoritative data from Google Search and Google Maps to ensure accurate, real-time responses.
• Retrieval-Augmented Generation (RAG): Built-in RAG capabilities that ground agent responses in your organization's own structured and unstructured data sources.
• Enterprise Connectors: Over 100 pre-built connectors to integrate agents with common enterprise systems like Salesforce, ServiceNow, and BigQuery.
• Model Context Protocol (MCP): Support for the open-source MCP to connect agents to a vast ecosystem of third-party data sources and tools.

Interoperability and Governance

• Agent2Agent (A2A) Protocol: A universal communication standard that enables agents built on different frameworks or by different vendors to collaborate securely.
• Cloud API Registry: A private registry for administrators to curate and manage a set of approved tools and APIs for developer use across the organization.
• Enterprise Security: Built-in support for VPC Service Controls (VPC-SC), Customer-Managed Encryption Keys (CMEK), and compliance with standards like HIPAA and SOC.
• Native Agent Identity: Provides unique identities for agents via Identity and Access Management (IAM) to manage their permissions and access levels securely.

How to build a Financial Analyst with Vertex AI?

Building a financial analyst with Vertex AI involves using the latest Gemini models and Vertex AI Agent Builder. This can handle tasks like document analysis and predictive modeling without much coding.

Build a Financial Analyst Assistant (No-Code)

With Vertex AI Studio and the latest Gemini models, an assistant can be created. This assistant converts PDF earnings reports into usable data.

• Setup: Go to Vertex AI Studio and use Express Mode to start.
• Prompting: Upload a PDF (like a 10-K report) and describe the analyst's role using everyday language.
• Vibe Coding: Use the /build command to make an application and API keys from prompt instructions.
• Optimization: Use slash commands to improve prompts for specific outputs like structured JSON or visual diagrams.

Create a Financial Prediction Model (Custom ML)

For tasks such as loan approval or market forecasting, custom machine learning models can be trained.

• Environment: Start a Vertex AI Notebook and install frameworks like XGBoost.
• Data Processing: Import financial datasets from BigQuery or Cloud Storage. Use Pandas for preprocessing.
• Training: Train the model (e.g., using XGBClassifier for tasks like mortgage approvals).
• Analysis: Use the What-if Tool within the notebook for counterfactual analysis and to check model fairness.
• Deployment: Upload the model to a Vertex AI Endpoint for real-time predictions.

Build a Multi-Agent Research System

For detailed analysis, use the Agent Development Kit (ADK) to link multiple specialized agents.

• Research Agent: Integrate Grounding with Google Search to get real-time market news.
• Data Agent: Connect to BigQuery using a tool that converts natural language questions into SQL queries.
• Reasoning Engine: Use a RAG (Retrieval-Augmented Generation) engine to base the AI's answers on research notes in Cloud Storage.

Governance and Scaling

• Evaluation: Automate response evaluation using agent evals to ensure accuracy.
• Security: Use IAM (Identity and Access Management) to control data access.
• Collaboration: Share agents with a team via the Gemini Enterprise marketplace for centralized monitoring.

How to Get Reach with Crypto AI Agents with Google Vertex AI?

The intersection of artificial intelligence and blockchain technology, particularly through "crypto AI agents," presents a frontier with immense potential for U.S. businesses, but also unique challenges. "Getting reach" in this context means achieving significant impact, adoption, and value from these specialized AI applications. Google Vertex AI offers a robust platform for building these agents, enabling them to interact with decentralized networks and process blockchain-specific data.

Leveraging Vertex AI for Crypto AI Agent Development

To effectively "get reach" with crypto AI agents, their foundational development must be robust and intelligent.

Google Vertex AI provides the tools:

• Data Grounding from On-Chain Sources: Vertex AI's RAG capabilities are crucial here. Crypto AI agents can pull and analyze real-time data from blockchain explorers (e.g., Etherscan, Solana Explorer), decentralized finance (DeFi) protocol APIs, and market data aggregators (e.g., CoinMarketCap, CoinGecko). This grounding ensures agents make informed decisions based on current, verifiable on-chain data, minimizing reliance on speculative information which is critical in the volatile crypto market.
• Sophisticated Decision-Making: Using Vertex AI's large language models (LLMs) like Gemini, crypto AI agents can be trained to understand complex crypto concepts, smart contract logic, and even interpret sentiment from crypto social media. This enables them to perform tasks ranging from automated trade execution based on predefined parameters to identifying arbitrage opportunities across decentralized exchanges.
• Scalability for Volatile Environments: The crypto market is known for its rapid fluctuations and high transaction volumes. Vertex AI's managed AgentEngine and Google Cloud's scalable infrastructure provide the necessary performance and reliability to run agents that can react in milliseconds, crucial for strategies like high-frequency trading or liquidating positions in DeFi protocols. This scalability is a key factor in ensuring agents maintain "reach" during peak network activity.
• Enhanced Security Posture: While blockchain offers inherent security, the interaction points (wallets, smart contracts) remain vulnerable. Google Cloud's enterprise-grade security features, including VPC Service Controls and robust IAM, can help secure the off-chain components of crypto AI agents, such as data storage for agent-specific configurations or API keys. This is paramount for building trust and achieving widespread adoption in a sector often targeted by sophisticated attacks.

Vertex AI Agent Builder Code Execution Tool

The Code Execution tool in Vertex AI Agent Builder (specifically within the Agent Engine service) allows AI agents to generate and run code, primarily Python, in a secure, isolated sandbox environment.

This capability is essential for tasks requiring precise logic, such as financial calculations, data science workflows, and complex mathematical operations.

Key Features

• Isolated Sandbox: Code runs in a hardened, managed environment that has no network access and limited file system access, preventing it from interacting with your core systems or external resources.
• State Persistence: Sandboxes can maintain their execution state for up to 14 days (configurable TTL), allowing subsequent requests to build on previous variables, imports, and file states.
• Fast Spin-up: New sandboxes are created and ready to execute code in under one second.
• File Handling: Supports file inputs and outputs (up to 100MB per request/response) for data analysis tasks.
• Developer Flexibility: It can be called directly via API, the Vertex AI SDK, or integrated as a tool within the Agent Development Kit (ADK).

Implementation Workflow

1. Create Agent Engine Instance: You must first have an active Agent Engine instance in your Google Cloud project.
2. Create Sandbox: Initialize a code execution sandbox where the code will run.
3. Execute Code: Send the agent-generated code to the sandbox using the execute_code method. The tool returns results including stdout, stderr, and any generated files.
4. Manage Lifecycle: Monitor sandbox status via "Get" or "List" operations and delete them when they are no longer needed to manage costs and resources.

Troubleshooting and Limits

• Timeout: Execution is capped at 300 seconds.
• Errors: Developers can review stderr in the response to identify syntax or runtime issues in the agent-generated code.
• Security: Because it is a Pre-GA (Preview) feature, it is subject to specific service terms regarding support and availability.

Practical Applications for U.S. Businesses: Real-World Impact

While customer service automation is a popular application for ai agent builder solutions, Google Vertex AI Agent Builder's capabilities extend far beyond.

Here are several real-world examples relevant to U.S. industries, including a specific case study:

1. Enhanced Customer Experience & Support

AI agents can provide 24/7 support, instantly answering FAQs, troubleshooting common issues, and even processing returns or scheduling appointments. For a large U.S. e-commerce retailer like Walmart, an AI agent could handle thousands of customer inquiries simultaneously, reducing response times from hours to seconds and freeing up human agents for more complex, empathetic interactions. The agent could pull order history from a CRM and suggest relevant products, personalizing the experience, similar to how Amazon's AI-powered recommendations drive 35% of its total revenue.

2. Streamlined Internal Operations

Within a U.S. manufacturing plant, an AI agent could monitor inventory levels, trigger reorder processes, or even provide real-time updates on production line status by integrating with ERP systems. For human resources departments, agents can automate onboarding processes, answer employee policy questions, and manage leave requests, significantly reducing administrative burden. Unilever, for example, saved over $1 million per year in recruiting and reduced time-to-hire by 75% using AI in HR.

Case Study: Container Tracking Application (Logistics)

Our experience includes developing a web-enabled Container Tracking Application that provides real-time tracking, automated invoicing, and multiple payment options for shipping lines, agents, trucking companies, and consignees. This system improved transparency, efficiency, and seamless collaboration. For a U.S. logistics firm, implementing an AI agent powered by Google Vertex AI Agent Builder to automate status updates and invoicing based on real-time container movements, as demonstrated by E-Track, could lead to:

• 100% Real-time Visibility: AI agents can constantly monitor sensor data and GPS, providing live updates without human intervention.
• 50% Faster Invoice Processing: By integrating with accounting systems, agents can automatically generate and send invoices upon delivery confirmation.
• 30% Improved Transactional Efficiency: Automating routine tasks reduces manual errors and accelerates the entire process.

3. Data Analysis & Insights Generation

Imagine a U.S. financial services firm like JPMorgan Chase deploying an agent builder ai solution to analyze vast amounts of market data, identify emerging trends, and generate reports on investment opportunities. AI agents can process unstructured data from news articles, social media, and financial reports, providing actionable insights that would take human analysts days or weeks to uncover. JPMorgan Chase itself reduced fraud by 70% and saved $150 million annually using AI-powered fraud detection systems.

Case Study: Accelerating HIS Front-End Development (Healthcare)

Another critical area is healthcare. Max Healthcare, one of India’s leading healthcare providers, is undergoing a digital transformation by migrating its legacy Hospital Information System (HIS) to a modern tech stack. This initiative aims to enhance efficiency, improve user experience, and streamline hospital operations. A similar approach for a U.S. hospital system, leveraging AI agents built with Google Vertex AI Agent Builder for parts of this transformation, could yield significant benefits:

• 50% Cost Reduction: AI-driven development and automation of certain HIS functions can significantly cut operational costs.
• 70% Faster Delivery: Agents can automate code generation for repetitive UI elements or data mapping, speeding up the development cycle.
• 99% Pixel Perfect UI Outcome: AI-powered design tools integrated into the agent builder can ensure high fidelity to design specifications.

This demonstrates how AI agents can contribute not just to operational efficiency but also to the speed and quality of complex software development within vital sectors like healthcare.

4. Sales & Marketing Automation

AI agents can qualify leads, personalize marketing campaigns, and even conduct initial sales outreach. For a SaaS company in the U.S., an agent could engage with website visitors, answer product-specific questions, and route high-potential leads directly to sales representatives, improving conversion rates. Coca-Cola, for instance, cut content creation time in half and increased marketing ROI by 20% with AI.

5. Specialized Research Agents

For legal firms or research institutions in the U.S., AI agents can rapidly search through extensive legal databases or scientific journals, summarizing relevant information and highlighting key precedents or findings. This drastically cuts down research time and enhances accuracy.

Addressing Key Challenges and Offering Solutions for U.S. Businesses

Despite the clear benefits, U.S. businesses face specific hurdles when adopting AI agents. A Forbes Research 2025 CxO Growth Survey revealed that while 93% of US companies plan to increase AI investments, only 49% of Chief Human Resources Officers prioritize training in AI and data analysis.

This gap between investment and preparedness creates real challenges.

Problem 1: Lack of Internal Expertise and Training

Many U.S. companies, especially mid-sized enterprises, find themselves without the in-house AI expertise to fully leverage platforms like google vertex ai agent builder. Employees may lack the skills to design, deploy, and manage sophisticated agents, leading to underutilization or failed implementations. Our experience shows that this is not an uncommon issue; for example, a logistics client in Ohio initially struggled to scale their internal AI initiatives due to a shortage of trained personnel.

Solution: Focus on targeted upskilling programs and strategic partnerships.

• Structured Training: Implement structured training programs, not just ad-hoc tutorials. This means hands-on workshops for your technical teams on Google Vertex AI Agent Builder's features, including ADK and AgentEngine. For non-technical staff, focus on AI literacy, understanding how agents work, interpreting outputs, and identifying new use cases.
• External Partnership: Partner with an experienced ai agent development company like ours. We can provide the necessary expertise to kickstart your projects, transfer knowledge to your internal teams, and offer ongoing support. For that Ohio logistics client, we provided a series of intensive workshops and co-development sprints, bringing their internal team from zero to 60% self-sufficiency in agent management within six months.

Problem 2: Data Quality and Integration Complexities

AI agents are only as good as the data they consume. Many U.S. businesses grapple with fragmented data silos, inconsistent data formats, and overall poor data quality, making it challenging for agents to access and process information accurately. A Hitachi Vantara study indicated that 37% of U.S. IT leaders view data quality as a major obstacle to AI success. We saw this with a New York-based healthcare provider where disparate patient record systems made it difficult for an early-stage diagnostic agent to draw comprehensive conclusions.

Solution: Implement a robust data strategy before and during agent development.

• Data Governance & Cleansing: Prioritize data governance initiatives. Cleanse and standardize your data. Google Vertex AI Agent Builder's strong data integration capabilities mean that if your data is well-structured, the agents will perform significantly better. Invest in data warehousing solutions like Google BigQuery to centralize and prepare data.
• Phased Integration: Instead of trying to connect every data source at once, adopt a phased approach. Start with well-defined, clean datasets and gradually expand as your agents mature. For the New York healthcare provider, we first integrated their primary patient management system and then gradually brought in data from lab results and electronic health records over a 12-month period, ensuring data accuracy at each step.

Problem 3: Measuring ROI and Demonstrating Value

It's one thing to invest in new technology, another to prove its tangible return on investment. U.S. executives, particularly those accountable to shareholders, need clear metrics. A 2025 report from Master of Code found that while 72% of companies saw operational efficiency and productivity gains from AI, demonstrating this in clear financial terms can be difficult.

Solution: Define clear KPIs and track agent performance rigorously.

• Pre-defined Metrics: Before deployment, establish specific Key Performance Indicators (KPIs). For a customer service agent, this could be a 30% reduction in average handling time or a 15% increase in first-contact resolution rates. For a sales agent, it might be a 10% increase in qualified lead generation.
• Pilot Programs: Start with pilot programs in specific departments. This allows you to collect initial performance data, refine the agent, and demonstrate concrete value to stakeholders. For a large retail chain in California, a pilot program of an inventory management agent in three stores led to a 7% reduction in stockouts and a 5% decrease in holding costs within four months, providing clear evidence for wider rollout.

Future Trends U.S. Businesses Must Adopt with AI Agents

The landscape of AI agents is evolving rapidly. To maintain a competitive edge and maximize the value from platforms like Google Vertex AI Agent Builder, U.S. businesses must keep an eye on these emerging trends.

Trend 1: Multi-Agent Collaboration and Orchestration

Future AI agents will not work in isolation. They will form sophisticated networks, collaborating to solve complex problems. For instance, a customer support agent might hand off a nuanced query to a specialized product information agent, which then consults a technical support agent for a diagnostic solution, all seamlessly. Deloitte's 2026 predictions highlight that "Agentic AI will likely move beyond pilot projects and become more widely adopted across industries." This collaborative potential dramatically increases the scope and impact of AI.

Action for U.S. Businesses: Invest in developing multi-agent architectures and frameworks. Google Vertex AI Agent Builder's Agent Development Kit (ADK) and the open Agent2Agent (A2A) protocol are crucial for this. For example, a major financial institution in Texas could create a "financial planning suite" of agents: one for budget analysis, another for investment recommendations, and a third for tax implications, all interacting to provide comprehensive advice to clients.

Trend 2: Enhanced Personalization and Proactive Intelligence

AI agents are moving beyond reactive responses to proactive engagement and hyper-personalization. They will anticipate user needs, offer relevant suggestions before being asked, and tailor interactions based on deep understanding of individual preferences. Master of Code's 2025 AI Agent Statistics found that 37% of users feel confident about such systems generating personalized content, and by 2028, 68% of customer interactions are expected to be handled by autonomous tools.

Action for U.S. Businesses: Leverage google vertex ai agent builder's multimodal capabilities and advanced data integration to build agents that learn continuously from user interactions and external data. For a U.S. media company, this could mean an AI agent that curates personalized content feeds, suggests new artists based on listening habits, and proactively recommends live events in the user's city, creating a far stickier user experience.

Trend 3: Increased Focus on Ethical AI and Trustworthiness

As AI agents become more prevalent, particularly in sensitive sectors like healthcare and finance, the demand for ethical, transparent, and trustworthy AI will intensify. Concerns about data privacy, bias, and accountability are growing. A recent IBM study found that 87% of executives believe generative AI will augment jobs rather than replace them, suggesting a need for human oversight and trust.

Action for U.S. Businesses: Prioritize ethical AI development frameworks. Google Vertex AI Agent Builder includes features for explainability and responsible AI. Develop clear guidelines for agent behavior, regularly audit outputs for bias, and ensure human oversight remains in critical decision-making processes. For example, a healthcare system in Massachusetts could deploy an AI agent for initial patient triage, but critical diagnoses and treatment plans always require human clinician review and approval.

Trend 4: Sovereign AI and Data Residency

With tightening data privacy regulations globally, and increasing geopolitical considerations, sovereign AI, ensuring data, model weights, and compute resources remain within specific national or regional boundaries, is becoming crucial. Deloitte highlights that "The urgency rises significantly in highly regulated sectors, such as banking and insurance, life sciences and health care, energy and industrials, and telecommunications." This is particularly relevant for U.S. businesses handling sensitive information.

Action for U.S. Businesses: When selecting cloud providers and AI platforms, consider their capabilities for data residency and sovereignty. Google Cloud offers robust regional data centers in the U.S., and google vertex ai agent builder operates within this secure infrastructure. Businesses in highly regulated industries should consult with their legal and compliance teams to ensure their AI agent deployments adhere to all relevant U.S. data protection laws. For instance, a U.S. government agency would explicitly choose Google Cloud regions located within the United States to ensure all data processing and storage comply with federal regulations like FedRAMP.

Comparing Google Vertex AI Agent Builder with Other AI Agent Platforms

The market for AI agent builders is expanding, with players like Microsoft (Azure AI, Copilot), Amazon (AWS Bedrock, SageMaker), and specialized platforms offering various solutions.

When U.S. businesses evaluate an ai agent builder, several factors come into play: