Vertex AI Agent: Architecture, Development & Deployment

Vertex AI Agent Builder is part of Google Cloud’s Vertex AI platform.  It’s the framework developers use to create and deploy Vertex AI Agents - gen AI agents that operate on enterprise data and run reliably in production. 

It offers both a no-code console and SDKs, so you can prototype quickly or build through code when you need more control. Agent Builder manages grounding, orchestration, and deployment, making it easier to connect large language models (LLMs) with real systems and data.

Let’s look at how Vertex AI Agent Builder works, how its parts fit together, and what’s involved in building and deploying an agent in production.

What Is Vertex AI Agent/Agent Builder?

Vertex AI Agent Builder is part of Google Cloud’s Vertex AI platform. It provides the tools and infrastructure to build, manage, and deploy AI agents that integrate with enterprise systems.

You can create agents with Google’s Agent Development Kit (ADK) or connect those built with open-source frameworks like LangChain or LangGraph. The platform supports both single and multi-agent setups, handling communication and coordination through the Agent2Agent (A2A) protocol.

It fits easily into existing environments, with prebuilt connectors for cloud services, APIs, and on-prem data. Core features include:

• ADK: A Python SDK for quick agent creation.

• Multi-agent orchestration: Define workflows and execution control.

• Agent Engine: Managed runtime for deployment and scaling.

• Memory and context: Short- and long-term context retention.

• Model Context Protocol (MCP): Connect agents to structured and unstructured data.

  
  

In short, Agent Builder gives you a practical framework to design and run autonomous, data-aware agents within a secure, production-ready environment.

Agent Builder vs Agent Engine

Agent Builder works closely with the Agent Engine, but they have distinct purposes:

Component	Purpose	Phase
Agent Builder	Used at design time to build, configure, and ground agents through the console or SDKs	Development
Agent Engine	Managed runtime that executes agents, handles requests, sessions, and scaling	Production

Agent Builder is where you design and test your agents. The Agent Engine is where those agents live and serve requests in production environments.

How Vertex AI Agent Components Work Together

Vertex AI Agent Builder includes several components that cover development, integration, and deployment of AI agents in production.

1. Agent Development Kit (ADK)

The ADK offers a code-first way to create production-ready agents in Python. It simplifies how you define reasoning, tools, and workflows while supporting collaboration between agents. You can build agents in fewer than 100 lines of code, simulate interactions locally, and deploy directly to the managed runtime.

It also supports bidirectional audio and video streaming for more natural interactions. Developers can explore ready-to-use templates and tools in the Agent Garden to accelerate development.

2. Agent2Agent (A2A) Protocol

The A2A protocol allows agents from different frameworks or vendors to communicate. It acts as an interface for agents to publish their capabilities and exchange data through text, forms, or audio/video. This makes multi-agent workflows interoperable across ecosystems such as Salesforce, ServiceNow, and UiPath.

3. Data & Tool Integration

Agents connect to enterprise systems through:

• Model Context Protocol (MCP): Accesses structured and unstructured data.

• Prebuilt connectors: Integrates with over 100 business applications.

• Apigee API Management: Uses existing APIs for agent actions.

• RAG integration: Retrieves relevant information from company data sources.

4. Agent Engine (Managed Runtime)

The Agent Engine handles deployment, scaling, and monitoring. It provides automatic infrastructure management, context retention (short- and long-term memory), and session continuity. Built-in evaluation tools help improve agent performance based on real usage data.

5. Open Framework Support

Developers can build agents with frameworks like LangChain, LangGraph, or Crew.ai and deploy them directly on Vertex AI without rewriting code. This flexibility lets teams use familiar tools while relying on Google’s managed infrastructure for reliability and scale.

6. Agentspace (Enterprise Platform)

Google Agentspace provides centralized governance, registration, and access management for deployed agents. It integrates with Gemini and Google Search to make agents discoverable and usable across the organization.

Building and Deploying Agents with Vertex AI Agent Builder

Vertex AI Agent Builder provides a clear path from designing an agent to deploying it in production. The workflow includes defining the purpose, developing the logic, connecting data, and managing deployment.

1. Design the Agent

Start by defining what the agent should do and how it fits into your system. Map its goals, tasks, and interactions with users or tools.

Use the Agent Development Kit (ADK) if you prefer a code-first approach in Python. It lets you define reasoning steps, workflows, and guardrails with precise control. For faster prototyping, you can use the no-code console or draw from templates in the Agent Garden.

2. Develop and Test

You can build agents using ADK or open frameworks like LangChain, LangGraph, or Crew.ai, then deploy them to Vertex AI without major adjustments.

 ADK provides SDKs and CLI tools for defining prompts, tools, and orchestration logic. It also supports audio and video streaming for interactive use cases.

 Before deploying, test the agent locally using ADK’s simulation tools or reference implementations from Agent Garden.

3. Connect Data and Tools

Agents often need access to company data and systems. Vertex AI supports this through:

• Model Context Protocol (MCP): Connects agents to structured and unstructured data sources.

• Prebuilt connectors: Integrations with common business systems (ERP, HR, CRM).

• Apigee: Manages custom APIs and tool integrations.

• Retrieval-Augmented Generation (RAG): Uses Vertex AI Search or custom retrieval pipelines to improve factual accuracy.

  
  

4. Enable Multi-Agent Collaboration

When working with multi-agent systems, the Agent2Agent (A2A) protocol allows communication between agents built with different frameworks. It defines a standard format for exchanging data and coordinating tasks, supporting text, forms, and audio/video interactions.

5. Deploy to Production

Once tested, deploy your agent to the Agent Engine, a managed runtime that handles scaling, context retention, and monitoring. It supports both short-term and long-term memory, ensuring consistent behavior across sessions.

 For enterprise setups, agents can be registered in Google Agentspace, which provides centralized governance and integration with Gemini and Google Search.

6. Monitor and Optimize

Vertex AI includes evaluation and telemetry tools to monitor agent performance. Use metrics and logs to refine decision logic, tool usage, and grounding quality over time.

In summary:

• Design: Define agent goals and workflow.

• Build: Develop with ADK or open frameworks.

• Connect: Integrate enterprise data and tools.

• Collaborate: Use A2A for multi-agent systems.

• Deploy: Run in Agent Engine with managed scaling.

• Optimize: Evaluate and improve with built-in monitoring tools.

Use Cases & Practical Examples

Vertex AI Agent Builder can be applied across different types of AI-driven systems. So, common patterns include:

• Conversational and support agents:Build chat-based or voice-enabled agents for internal knowledge access, document assistance, or guided workflows. They can connect to web apps, help desks, or collaboration tools to deliver grounded, context-aware responses.

  
  

• Workflow automation:Automate structured business processes such as document processing, approval routing, and data validation. Agents can call APIs, connect to enterprise systems like ERP or HR platforms, and extend existing workflows through Application Integration or Apigee.

  
  

• Multi-agent workflows:Use the Agent2Agent (A2A) protocol to let agents from different frameworks collaborate. One agent can handle user interaction while another performs analysis or retrieval. A2A supports flexible communication formats, including text, forms, and audio/video.

  
  

• Monitoring and optimization:Use tracing, logging, and visualization tools to debug and analyze agent behavior. Refine reasoning and improve reliability over time. Register agents in Google Agentspace for centralized management and access control.

Best Practices & Common Pitfalls

Building with Vertex AI Agent Builder works best when the setup and workflow are well thought out. Below are the key practices that help teams build stable, secure agents, and the common issues to watch for.

Best Practices

1. Start with a clear use case: Define exactly what problem the agent solves. Keep the scope narrow for more reliable performance.

2. Use the right development tools: Build with the Agent Development Kit (ADK) for fast, production-ready setup, or use open-source frameworks like LangChain, LangGraph, or Crew.ai if your team prefers them.

3. Plan for interoperability: Use the Agent2Agent (A2A) protocol so agents from different frameworks can communicate and collaborate without rebuilding.

4. Connect data securely: Integrate enterprise systems through Model Context Protocol (MCP) and prebuilt connectors. Use Apigee for managing and securing API access.

5. Ground responses in reliable data: Implement Retrieval-Augmented Generation (RAG) with Vertex AI Search or a custom RAG pipeline.

6. Monitor and refine continuously: Use tracing, logging, and evaluation tools in Agent Engine to debug and optimize performance.

7. Maintain governance and control: Register agents in Google Agentspace for consistent versioning, access control, and compliance.

Common Pitfalls

Even experienced teams hit a few snags with multi-agent systems. These are the ones that come up most often:

• Building without a clearly defined purpose or boundaries.

• Granting overly broad data or API permissions.

• Skipping observability and debugging early in testing.

• Ignoring context or memory limits, leading to drift in responses.

• Overlooking operational costs tied to Agent Engine compute and memory.

• Assuming every external system supports A2A today, it’s still maturing.

Getting Started

If you already use Google Cloud, Vertex AI Agent Builder is a practical way to build and manage agents. It connects cleanly with other Google Cloud services and handles deployment and scaling for you.

A few things to note: it runs fully on Google Cloud, pricing is based on usage, and the SDK currently supports Python.

The best approach is to start small - build one agent, connect it to real data, and test how it performs before expanding.

You can connect to our AI engineering team to discuss how best to integrate agents into your systems or design a setup that fits your existing workflows.

Frequently Asked Questions

What is Vertex AI Agent Builder?

Vertex AI Agent Builder is Google Cloud’s framework for building, configuring, and deploying generative AI agents. It offers both a no-code interface and SDKs (mainly Python), integrates with enterprise data, and manages grounding, orchestration, and deployment through a fully managed runtime.

What are the 4 types of AI agents?

The main types of AI agents are:

• Simple reflex agents: respond directly to inputs using predefined rules.

• Model-based reflex agents: use an internal model to handle hidden or changing states.

• Goal-based agents: choose actions that move toward a specific goal.

• Utility-based agents: act to maximize an overall measure of usefulness or value.

 (Some approaches also include learning or hierarchical agents that build on these types.)

What are the top 4 AI agent frameworks?

 The most common open frameworks for building AI agents are:

• LangChain: modular orchestration with built-in memory and tool support.

• LangGraph: graph-based framework for structured multi-agent workflows.

• CrewAI: enables role-based collaboration among multiple agents.

• Microsoft Semantic Kernel: SDK for integrating agents into applications and workflows.