Vertex AI Agent Builder

Vertex AI Agent Builder is Google Cloud’s managed platform for building and running agents in production. Where a framework gives you code, Agent Builder gives you infrastructure: it bundles the Agent Development Kit for authoring, Agent Engine — a fully managed runtime that hosts your agent with sessions, memory, and scaling handled for you — plus grounding tools like Google Search and Vertex AI Search over your own documents, all inside your Google Cloud project.

Its value is everything that happens after your agent works on a laptop. Deploying an agent for real means session storage, identity and access control, observability, evaluation, and compliance — the undifferentiated heavy lifting Agent Builder absorbs. Because it lives inside Google Cloud, you inherit IAM, VPC controls, logging, and monitoring instead of assembling them, which is exactly what enterprise teams need to get an agent past a security review. It’s also comparatively framework-open: Agent Engine can host agents written in ADK, LangGraph, CrewAI, or LlamaIndex, so choosing the platform doesn’t fully lock in your authoring layer — though you are committing to Google Cloud as the runtime.

In practice you’ll build the agent locally (typically with ADK), test it, then deploy with the Vertex AI SDK — roughly agent_engines.create(agent, requirements=[...]) — and call it via SDK or REST from your application. From the console you manage deployed agents, run evaluations, inspect traces, wire up grounding data stores, and connect enterprise systems through built-in connectors and MCP.