GCP – More ways to build, scale, and govern AI agents with Vertex AI Agent Builder

Many developers are prototyping AI agents, but moving to a scalable, secure, and well-managed production agent is far more complex. 

Vertex AI Agent Builder is Google Cloud’s comprehensive and open platform to build, scale, and govern reliable agents. As a suite of products, it provides the choice builders need to create powerful agentic systems at global scale. 

Since Agent Builder’s public inception earlier this year, we’ve seen tremendous traction with components such as our Python Agent Development Kit (ADK), which has been downloaded over 7 million times. Agent Development Kit also powers agents for customers using Gemini Enterprise and agents operating in products across Google. 

Today, we build on that momentum by announcing new capabilities across the entire agent lifecycle to help you build, scale, and govern AI agents. Now, you can: 

• Build faster with control agent context and reduce token usage with configurable context layers (Static, Turn, User, Cache) via the ADK API.

• Scale in production with new managed services from the Vertex AI Agent Engine (AE) including new observability and evaluation capabilities 

• Govern agents with confidence with new features including native agent identities and security safeguards

These new capabilities underscore our commitment to Agent Builder, and simplify the agent development lifecycle to meet you where you are, no matter which tech stack you choose. 

For reference, here’s what to use, and when:

1. Build your AI agents faster

Building an agent from a concept to a working product involves complex orchestration. That’s why we’ve improved ADK for your building experience:

• Build more robust agents: Use our adaptable plugins framework for custom logic (like policy enforcement or usage tracking). Or use our prebuilt plugins, including a new plugin for tool use that helps agents ‘self-heal.’ This means the agent can recognize when a tool call has failed and automatically retry the action in a new way.

• More language support: We are also enabling Go developers to build ADK agents (with a dedicated A2A Go SDK) alongside Python and Java, making the framework accessible to many more developers.

• Single command deployment: Once you have built an agent, you can now use the ADK CLI to deploy agents using a single command, adk deploy,to the Agent Engine (AE) runtime. This is a major upgrade to help you move your agent from local development to  live testing and production usage quickly and seamlessly.

You can start building today with adk-samples on GitHub or on Vertex AI Agent Garden – a growing repository of curated agent samples, solutions, and tools, designed to accelerate your development and support one click deployment of your agents built with ADK.

2. Scale your AI agents effectively

Once your agent is built and deployed, the next step is running it in production. As you scale from one agent to many, managing them effectively becomes a key challenge. That’s why we continue to expand the managed services available in Agent Engine. It provides the core capabilities for deploying and scaling the agents you create in Agent Builder

• Observability: We’re bringing the local development environment that you know and love from adk web to Google Cloud to enable Cloud based production monitoring. Within Agent Engine, we are making it easy to: 

• Track key agent performance metrics with a dashboard that measures token consumption, latency, error rates, and tool calls over time. 

• Find and fix production issues faster in a traces tab so you can dive into flyouts to visualize and understand the sequence of actions your agents are taking. 

• Interact with your deployed agent (including past sessions or issues) with a playground to dramatically shorten your debug loop.

Below is a demo showcasing the new observability features in actions such as an updated AE dashboard, traces, and playground within Agent Engine

3. Govern your AI agents with confidence

Now that you can measure your agent performance at scale the final stage of the lifecycle is ensuring they operate safely and responsibly. New and expanded capabilities include:

• Agent identities: Building on our existing Cloud IAM capabilities, we are giving agents their own unique, native identities within Google Cloud. As first-class IAM principals, agent identities allow you to enforce true least-privilege access, establish granular policies, and resource boundaries to meet your compliance and governance requirements. 

• Safeguards and advanced security: Existing protections are already available to protect and secure AI applications. Model Armor provides protection against input risks like prompt injection, while also screening tool calls and agent responses. For complete control, Model Armor provides built-in inline protection for Gemini models and a REST API to integrate with your agents. To provide full visibility, new integrations with AI Protection in Security Command Center will discover and inventory agentic assets as well as detect agentic threats such as unauthorized access and data exfiltration attempts by agents.

As a bonus, agents you build in Agent Builder can be registered for your teams to use directly within Gemini Enterprise. 

Below is a mock of a dashboard in Gemini Enterprise, showing how custom agents built in Agent Builder can be registered and made available to your employees, creating a single place for them to accelerate their workflows.

How customers are achieving more with Agent Builder

“Color Health, with its affiliated medical group Color Medical, operates the nation’s only Virtual Cancer Clinic, delivering clinically guided, end-to-end cancer care across all 50 states, from prevention to survivorship. In partnership with Google Cloud and Google.org, we’re helping more women get screened for breast cancer using an AI-powered agent built with Vertex AI Agent Builder using ADK powered by Gemini LLMs and scaling them into production with Agent Engine. The Color Assistant determines if women are due for a mammogram, connects them with clinicians, and schedules care. The power of the agent lies in the scale it enables, helping us reach more women, collect diverse and context-rich answers, and respond in real time. Early detection saves lives: 1 in 8 women develop breast cancer, yet early detection yields a 99% survival rate. Check it out here: color.com/breast-cancer-screening” – Jayodita Sanghvi, PhD., Head of AI Platform, Color

“PayPal uses Vertex AI Agent Builder to rapidly build and deploy agents in production. Specifically, we use Agent Development Kit (ADK) CLI and visual tools to inspect agent interactions, follow state changes, and manage multi-agent workflows. We leverage the step-by-step visibility feature for tracing and debugging agent workflows. This lets the team easily trace requests/responses and visualize the flow of intent, cart, and payment mandates. Finally, Agent Payment Protocol (AP2) on Agent Builder provides us the critical foundation for trusted agent payments. AP2 helps our ecosystem accelerate the shipping of safe, secure agent-based commerce experiences.” – Nitin Sharma, Principal Engineer, AI

“Geotab uses Vertex AI Agent Builder to rapidly build and deploy agents in production. Specifically, we use Google’s Agent Development Kit (ADK) as the framework for our AI Agent Center of Excellence. It provides the flexibility to orchestrate various frameworks under a single, governable path to production, while offering an exceptional developer experience that dramatically accelerates our build-test-deploy cycle. For Geotab, ADK is the foundation that allows us to rapidly and safely scale our agentic AI solutions across the enterprise” – Mike Bench, Vice President, Data & Analytics

Get started

Vertex AI Agent Builder provides the unified platform to manage the entire agent lifecycle, helping you close the gap from prototype to a production-ready agent. To explore these new features, visit the updated Agent Builder documentation to learn more.

If you’re a startup and you’re interested in learning more about building and deploying agents, download the Startup Technical Guide: AI Agents. This guide provides the knowledge needed to go from an idea to prototype to scale, whether your goals are to automate tasks, enhance creativity, or launch entirely new user experiences for your startup.