Google Vertex AI Agent Builder: The Enterprise AI Agent Platform Backed by Google Cloud's Full Stack | Review

Google Vertex AI Agent Builder is the enterprise AI agent platform that lets organisations build, deploy, and govern production-grade agents grounded in their own data, backed by Google Cloud's infrastructure, Gemini models, and the open Agent-to-Agent (A2A) protocol.

Founded 2011 (Google Cloud) | HQ: Mountain View, CA | Parent: Alphabet (GOOGL) | 12 percent global cloud market share

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Overview

Google launched Vertex AI Agent Builder as an evolution of its Dialogflow CX platform, consolidating agent creation, orchestration, and deployment into a single enterprise-grade product within Vertex AI. The platform sits at the centre of Google's agentic AI strategy, connecting Gemini foundation models, the open-source Agent Development Kit (ADK), and the Agent-to-Agent (A2A) protocol into a unified stack.

The ADK has been downloaded over seven million times since launch, with developers building production agents in under 100 lines of Python. Enterprise adoption is accelerating: Google Cloud reported a 20x increase in Vertex AI usage year over year. PwC has built over 120 AI agents on Google Cloud spanning 24 cross-functional workflows. Wells Fargo is an early adopter of the connected Agentspace product. Google was recognised as a Leader in the 2025 Gartner Magic Quadrant for AI Application Development Platforms.

What We Like

The A2A protocol gives agents a common language. Where most platforms lock agents into proprietary communication, Google open-sourced the Agent-to-Agent protocol to let agents from different vendors discover and collaborate with each other. Now at version 0.3 with gRPC support, security card signing, and a dedicated Python SDK, A2A is attracting cross-platform adoption from Salesforce, SAP, and others. This is the interoperability layer the market has been missing.

Full-stack enterprise integration out of the box. Vertex AI Agent Builder ships with 100+ enterprise connectors managed through Apigee, covering ERP, procurement, HR, and CRM systems. Agents connect natively to BigQuery for analytics, Google Workspace for productivity workflows, and the broader Google Cloud data stack. For organisations already on Google Cloud, time-to-value is measured in days, not months.

Agent Engine brings production-grade lifecycle management. The Agent Engine provides a fully managed runtime with sessions, memory bank, code execution, and evaluation tooling, all now generally available. This is the gap between demo and deployment that trips up most agent projects: state management, context persistence across interactions, and production monitoring. Google has made it a managed service.

The commerce stack is a differentiator. Google is the only hyperscaler with a dedicated commerce protocol for AI agents. The Universal Commerce Protocol (UCP) connects agents to the full purchase lifecycle, while AP2 handles secure agent-led payments. As we explored in our review of Google UCP, the protocol has attracted Shopify, Walmart, Target, Visa, and Mastercard. No other agent platform offers this end-to-end commerce layer.

What to Watch

Google Cloud lock-in is real. Vertex AI Agent Builder runs on Google Cloud. While the ADK is open-source and the A2A protocol is vendor-neutral, the managed services, connectors, and Agent Engine are tightly coupled to GCP. Organisations evaluating multi-cloud strategies should weigh the switching costs carefully. AWS Bedrock and Azure AI offer competing agent tooling within their own ecosystems.

Pricing complexity can surprise. Agent Engine charges per vCPU-hour ($0.0864) and per GB-hour ($0.0090), plus separate fees for sessions, memory bank events ($0.25 per 1,000), data storage ($1.00 per GB/month), and the underlying Gemini model tokens. There is no simple per-agent price. As we noted in our analysis of the agentic commerce stack, cost predictability remains a challenge for agent workloads at scale.

Third in cloud market share. Google Cloud holds approximately 12 percent of the global cloud market, behind AWS at 30 percent and Azure at 22 percent. While Google Cloud is the fastest-growing of the three (28 percent year-over-year in Q4 2025), Azure's OpenAI partnership and 60,000+ Azure OpenAI customers create significant competitive gravity.

Pricing and Deployment

Google Vertex AI Agent Builder uses consumption-based pricing. Agent Engine runtime costs $0.0864 per vCPU-hour and $0.0090 per GB-hour. Sessions and memory bank charge $0.25 per 1,000 events. New customers receive a $300 free credit valid for 90 days. An Express Mode allows limited experimentation with up to 10 agent engines for 90 days without enabling billing. Deployment is cloud-hosted on Google Cloud, with regional availability across seven additional regions as of early 2026.

Compliance and Security

Google Cloud maintains SOC 2 Type II, ISO 27001, ISO 27017, ISO 27018, FedRAMP Authorization to Operate, HIPAA compliance, and PCI DSS certification. This is the broadest compliance portfolio of any AI agent platform we have reviewed. Vertex AI inherits all Google Cloud platform-level certifications, and Gemini Enterprise includes documented compliance controls for enterprise data handling.

Verdict

Google Vertex AI Agent Builder is the strongest choice for enterprises that want to build production AI agents with enterprise-grade compliance, deep data integration, and interoperability through the A2A protocol. If your organisation is already on Google Cloud, or if you need agents that connect to commerce workflows via UCP, this platform offers capabilities no competitor matches. Teams committed to multi-cloud or those seeking simpler, predictable pricing should evaluate AWS Bedrock or open-source alternatives like CrewAI. With the fastest cloud revenue growth among hyperscalers, a Gartner Leader position, and the only open commerce protocol for AI agents, Google is positioning Vertex AI as the enterprise agent platform of record.

Try Google Vertex AI Agent Builder: cloud.google.com/products/agent-builder

Editorial disclaimer: Reviews reflect the independent editorial assessment of Major Matters and are not sponsored or endorsed by the companies reviewed. We recommend conducting your own evaluation to determine whether any product is the right fit for your specific requirements.

Charlie Major is a Product Development Manager at Mastercard. The views and opinions expressed in Major Matters are his own and do not represent those of Mastercard.