Vertex AI Agent Builder: Build, Deploy, and Scale AI Agents (2026)

In modern enterprises, AI is moving beyond individual models toward systems that involve multiple agents working together. This approach enables complex automation, richer interactions, and integration across diverse workflows. Vertex AI Agent Builder offers a structured way to build, connect, and deploy these systems at scale while allowing teams to keep flexibility in tools and frameworks.

Let's look at what Vertex AI Agent Builder offers, its core features, how it works, what it costs to run, and where it fits into an enterprise AI strategy.

What Is Vertex AI Agent Builder?

Vertex AI Agent Builder is a Google Cloud platform for building and orchestrating multi-agent systems for enterprise use. It integrates with existing processes and technology stacks, regardless of where you are in your AI adoption.

The platform reduces infrastructure complexity while keeping agent development flexible. You can build agents with Google's Agent Development Kit (ADK), use open-source frameworks such as LangChain or LangGraph, or connect agents created with other tools.

A note on naming before you go looking for it in the console: Google has been consolidating this surface under the Gemini Enterprise brand. Agentspace became Gemini Enterprise in late 2025, and at Google Cloud Next in April 2026 the developer platform was folded into what Google's documentation now calls the Gemini Enterprise Agent Platform, labeled "formerly Vertex AI". The capabilities described here are the same ones, and most teams still say Vertex AI in conversation, so expect to see both names for a while.

Vertex AI Agent Builder gives teams the tools to create agents that perceive their environment, reason about tasks, and operate autonomously. Its capabilities include:

• Agent Development Kit (ADK): Build production-ready agents in under 100 lines of Python code.
• Multi-agent orchestration: Apply deterministic guardrails and workflow controls.
• Agent2Agent** (A2A) protocol**: Connect agents across frameworks and vendors.
• Enterprise integration: Access systems and data through pre-built connectors.
• Agent Engine: Deploy and scale agents in a managed runtime.
• Audio/video streaming: Support natural, human-like conversations.
• Context retention: Maintain short-term and long-term memory.
• Model Context Protocol** (MCP)**: Connect to diverse enterprise data sources.

Vertex AI Agent Builder is designed for you if you need scalable, auditable AI systems integrated into your workflows.

How It Differs From Other Agent Builders

Vertex AI Agent Builder offers flexible development and strong enterprise integration. Unlike platforms locked to a single framework, Vertex supports multiple approaches.

With the Agent Development Kit (ADK), you can build production-ready agents with minimal code, control reasoning and interaction, and use bidirectional audio/video streaming for natural conversations.

You can also develop agents with LangChain, LangGraph, AG2, or CrewAI and deploy them on Vertex AI without rewriting code, reusing existing expertise and Google's managed infrastructure.

The Agent2Agent (A2A) protocol lets agents built on different frameworks and by different vendors communicate. A2A is now a Linux Foundation project rather than a Google-only effort, and the Foundation reported more than 150 organizations backing the standard at its one-year mark in April 2026, among them Box, Deloitte, Elastic, Salesforce, ServiceNow, and UiPath.

Compared to Microsoft Copilot Studio and AWS Bedrock, Vertex offers framework flexibility and multi-agent orchestration while providing a managed runtime that simplifies deployment and scaling.

Core Features & Architecture

Vertex AI Agent Builder separates agent development, communication, data access, and operations. This lets you build both single-agent applications and complex multi-agent systems without mixing concerns.

Agent Design & Multi-agent Orchestration

The Agent Development Kit (ADK) is the code-first path into multi-agent systems, with deterministic guardrails and orchestration controls for precise behavior. Agent Garden provides reusable patterns and components to speed development.

You can orchestrate workflows that combine specialized agents for tasks such as document processing, approval routing, and data validation while keeping compliance requirements intact. ADK manages short-term and long-term memory so agents retain context across interactions.

Communication Between Agents

The Agent2Agent (A2A) protocol enables communication across frameworks. It lets agents:

• Publish capabilities for discovery.
• Negotiate formats such as text or bidirectional audio/video.
• Maintain context across systems.
• Work securely under enterprise governance.

A2A removes integration barriers and supports collaboration across teams without rebuilding systems. Because the specification is governed outside any single vendor, adopting it does not tie your agents to one platform.

Data Grounding & Knowledge Integration

Vertex AI supports retrieval-augmented generation (RAG) for intelligent data access. Vertex AI Search offers ready-to-use RAG. Vector Search supports hybrid searches for precision.

Custom RAG engines connect to sources like:

• Local files, Cloud Storage, Google Drive
• Slack, Jira, other enterprise systems

Model Context Protocol (MCP) extends data access to the wider ecosystem of MCP-compatible tools, and the pre-built connector library covers ERP, HR, procurement, and other business systems. Apigee** integration** enables secure API reuse.

Security, Compliance & Guardrails

Vertex AI Agent Builder inherits Google Cloud's security framework. Agents run in IAM-controlled environments. VPC Service Controls limit network access. Audit logs track all interactions.

Content filters and deterministic guardrails allow precise behavior control. On the compliance side, the platform sits inside Google Cloud's attestations, which cover SOC 2 and ISO 27001, and HIPAA workloads are supported under a Business Associate Agreement. Confirm current scope for your specific services in Google's compliance documentation before you design against it.

Deployment, Monitoring & Scaling

The Agent Engine offers a managed runtime for deployment, handling infrastructure, scaling, and monitoring.

Framework-agnostic deployment supports ADK, LangChain, LangGraph, and others without rework. Context management preserves conversation continuity.

Monitoring tools include:

• Request tracing
• Performance metrics
• Reasoning logs
• Example Store for improvement

Autoscaling adjusts resources automatically. Gemini Enterprise, the platform Google previously called Agentspace, lets you register agents for organization-wide discovery and governance.

How to Build an Agent on Vertex AI

Building an agent involves choosing your development approach, defining behavior, connecting to the tools and data it needs, then testing and deploying. The process is iterative based on how the agent performs.

Steps to Build an Agent

1. Set up your environment: Create a Google Cloud project, enable the Vertex AI API, and install the latest Vertex AI SDK. Confirm you have the necessary IAM permissions and billing configured.
2. Develop an agent: Build with your preferred framework. Agent Engine offers full integration for ADK, LangChain, and LangGraph, SDK integration for AG2 and LlamaIndex, and custom templates for CrewAI and other frameworks. ADK ships templates and guardrails for faster development.
3. Deploy the agent: Use the Agent Engine managed runtime. You can customize the agent container image with build-time scripts and configure security settings such as IAM and VPC Service Controls.
4. Use the agent: Interact with it through API requests. Agents reach models, tools, and external systems through connectors or the Model Context Protocol (MCP).
5. Manage the deployed agent: Monitor performance with Cloud Monitoring and Cloud Trace, read interaction logs in Cloud Logging, and handle lifecycle operations such as updates or deletion through the console or API.

The Agent Starter Pack bundles templates, a testing playground, CI/CD pipelines, and observability tools if you would rather not assemble that scaffolding yourself.

Integrating with Tools, APIs & Data Sources

Vertex AI Agent Builder supports multiple integration patterns:

1. Model Context Protocol (MCP): ADK's MCP support connects agents to diverse data sources and capabilities through the growing ecosystem of MCP-compatible tools.
2. 100+ pre-built connectors: Connect to enterprise systems including ERP, CRM, HR platforms, procurement systems, and business applications without custom integration work.
3. Apigee API Management: Agents access existing API investments managed in Apigee, regardless of where those APIs reside. This reuses established API governance and security.
4. Application Integration workflows: Reuse existing workflows for orchestration logic, approval routing, and business process automation.
5. RAG data sources: Connect to local files, Cloud Storage, Google Drive, Slack, Jira, BigQuery, and other sources for grounding agent responses.
6. Custom APIs: Integrate your own REST APIs by defining function schemas. Agents learn when to call these APIs based on descriptions and parameters.

Authentication is managed through service accounts, with permissions granted for each resource the agent needs to access.

Use Cases & Applications

Vertex AI Agent Engine enables practical, data-driven AI solutions by connecting agents to APIs, databases, tools, and frameworks. Key applications include:

1. Public API integration: Build agents that access live data, such as currency exchange rates or project planning using geographic and supplier information.
2. Database interaction: Create agents that query structured, graph, or vector databases via AlloyDB, Cloud SQL, or other connectors for conversational search and insight generation.
3. Framework-based development: Use the Agent Development Kit (ADK) or open-source frameworks like LangGraph and OneTwo to design, deploy, and optimize agents. Multi-agent systems can be built using the Agent2Agent protocol.
4. Context management: Store and retrieve conversation history and context through Sessions and Memory Bank for improved personalization and continuity.
5. Monitoring and optimization: Track agent performance and behavior using built-in tools like Cloud Trace and OpenTelemetry.

These capabilities make Vertex AI Agent Engine suitable for automation, conversational AI, and intelligent reasoning across enterprise systems.

Best Practices & Common Pitfalls

Building on Vertex AI Agent Builder goes well when the setup and workflow are thought through before the first deployment. The practices below are what separate agents that stay stable from ones that need constant attention.

Start with a use case narrow enough to state in a sentence. Scope is the single biggest predictor of whether an agent performs reliably, and a broad mandate is the most common reason one does not. Pick your development path deliberately: ADK if you want the fastest route to a production-ready setup, or LangChain, LangGraph, or CrewAI if your team already works in them. Plan for interoperability early by using the A2A protocol, so agents built on different frameworks can work together without a rebuild later. Connect data through MCP and the pre-built connectors, and put API access behind Apigee so credentials and rate limits are managed in one place. Ground responses with RAG, either through Vertex AI Search or a custom retrieval pipeline, so answers trace back to your own data. Then keep tracing, logging, and evaluation switched on in Agent Engine from the first release rather than adding them after something goes wrong, and register agents centrally for versioning, access control, and compliance.

The pitfalls are the mirror image of that list, and these are the ones teams hit most often:

• Building without a clearly defined purpose or boundaries.
• Granting broader data or API permissions than the agent actually needs.
• Skipping observability and debugging early in testing.
• Ignoring context and memory limits, which shows up later as drift in responses.
• Leaving Agent Engine compute and memory out of the budget, so running costs arrive as a surprise.
• Assuming every external system already speaks A2A. Adoption is growing but still uneven.

Benefits of Using Vertex AI Agent Builder

The features above are the platform's mechanics. Four things are worth calling out separately as reasons teams choose it over assembling the same stack themselves:

1. Native Google Cloud integration: BigQuery, Cloud Storage, and other Google Cloud services connect without integration work if you are already on the platform.
2. Managed operations: Agent Engine handles infrastructure, scaling, security, and monitoring, so your team's attention goes to what the agent does rather than to what keeps it running.
3. Enterprise governance: Audit logging, VPC support, compliance coverage, and IAM integration are built in rather than bolted on.
4. Debugging and optimization: Tracing, visualization tools, and the Example Store let you improve agent behavior based on real usage instead of guesswork.

Limitations & Drawbacks

1. Google Cloud dependency: Agent Engine deployment requires Google Cloud. If you need multi-cloud or on-premises deployment, you will need alternative hosting, though you can still develop with ADK or other frameworks locally.
2. Cost considerations: Managed services carry premium pricing. Agent Engine bills separately for compute and memory on top of model usage, and session and memory storage are metered too, so price out expected volume against current published rates before you commit.
3. Learning curve: Teams unfamiliar with Google Cloud need to learn IAM, VPC, and platform concepts. Framework flexibility reduces this on the development side.
4. A2A ecosystem maturity: The protocol is established and widely backed, but not every external system you want to reach supports it yet.
5. Latency considerations: Managed infrastructure adds latency compared with a highly optimized custom implementation. That matters for latency-sensitive applications.
6. Naming churn: Google has renamed and reorganized this surface more than once in the past year. Product names, console paths, and documentation URLs move, so verify against current docs rather than against a guide, including this one.

Getting Started

Vertex AI Agent Builder is a solid choice if your environment is already on Google Cloud and you need flexible multi-agent orchestration with built-in integrations. Keep in mind its Google Cloud dependency and its usage-based cost structure.

You can also look at alternatives such as Microsoft Copilot Studio, AWS Bedrock, LangGraph, or CrewAI, depending on your infrastructure requirements and how your team prefers to work.

The most reliable way in is to start small: build one agent, connect it to real data, and see how it performs before expanding the scope.

If you are looking to integrate AI into your existing systems, whether building from scratch or extending current workflows, you can connect to our team to discuss the best approach.

Frequently Asked Questions

What is Vertex AI Agent Builder?

Vertex AI Agent Builder is a platform within Google Cloud for building and orchestrating enterprise-grade multi-agent systems. It provides the Agent Development Kit (ADK) for rapid agent creation, supports frameworks such as LangChain and LangGraph, and includes Agent Engine for fully managed deployment. The platform also carries the Agent2Agent (A2A) protocol for connecting agents across different vendors and frameworks. Google now brands the wider developer platform as the Gemini Enterprise Agent Platform, so you will see both names in documentation and in the console.

How do you build an agent in Vertex AI?

Build agents with the Agent Development Kit, or with frameworks such as LangChain, LangGraph, AG2, or CrewAI. Connect them to enterprise systems through the pre-built connectors, Apigee-managed APIs, or Application Integration workflows. Configure data grounding using Vertex AI Search, Vector Search, or the Model Context Protocol, then deploy to Agent Engine for managed infrastructure, scaling, and monitoring. Google's documentation provides step-by-step tutorials, and Agent Garden offers ready-to-use samples to start from.

What is an agent builder?

An agent builder is a platform or framework for creating AI agents, meaning systems that perceive their environment, reason about actions, and carry out tasks with limited supervision. Agent builders provide tools for defining agent behavior, connecting to data sources, orchestrating multi-agent workflows, and deploying agents to production. They range from low-level frameworks that leave infrastructure to you, through to fully managed platforms that handle deployment and operations.

What is the best AI agent building platform?

It depends on your needs. Vertex AI Agent Builder is strong for enterprise integrations, framework flexibility, and managed infrastructure, with A2A support that keeps you from being locked to one vendor. Alternatives such as Microsoft Copilot Studio, AWS Bedrock, LangGraph, and CrewAI suit different infrastructure requirements and team preferences. Choose based on your existing stack, your team's skills, and whether you would rather buy a managed service or keep full control.

What are the 4 types of AI agents?

Classic AI teaching divides agents into four types by how much internal state and reasoning they carry. Simple reflex agents respond directly to their current input using predefined rules. Model-based reflex agents keep an internal model of the world so they can act on states they cannot observe directly. Goal-based agents choose actions that move toward a stated objective rather than reacting input by input. Utility-based agents go a step further and weigh options against a measure of overall usefulness, which lets them trade off competing considerations. Some taxonomies add learning agents, which improve from experience, and hierarchical agents, which delegate to sub-agents, as extensions of the same four.

What are the top 4 AI agent frameworks?

The frameworks that show up most often in production work are LangChain, LangGraph, CrewAI, and Microsoft Semantic Kernel. LangChain offers modular orchestration with memory and tool support and has the widest integration surface. LangGraph models agent workflows as graphs, which suits structured multi-agent flows where you need explicit control over the path taken. CrewAI organizes several agents into defined roles that collaborate on a task. Semantic Kernel is an SDK aimed at embedding agents into existing applications and business workflows. All four run on Vertex AI Agent Engine, so the choice is about how your team wants to model the work rather than about where it can be deployed.