Cloud generative AI platform

How to use
Vertex AI.

Building and operating production generative AI and ML systems with Gemini and Model Garden models, grounding, RAG, evaluation, tuning, agents, and Google Cloud controls.

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What it isGoogle Cloud platform for generative AI models, agents, retrieval, tuning, evaluation, and ML operations Workflows2 UpdatedJuly 2026

Vertex AI is a Google Cloud platform for generative AI models, agents, retrieval, tuning, evaluation, and ML operations. Building and operating production generative AI and ML systems with Gemini and Model Garden models, grounding, RAG, evaluation, tuning, agents, and Google Cloud controls. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

01

Access & setup

Find, install, and sign in to Vertex AI

Get into the official Vertex AI experience with the right account and a setup you understand.

  1. 01

    Start at https://cloud.google.com/vertex-ai and confirm the domain before entering account or payment information.

  2. 02

    Availability: Vertex AI is accessed through the Google Cloud console, regional APIs, Google Gen AI and Vertex AI SDKs, CLI, notebooks, and managed services.

  3. 03

    A Google Cloud project with billing, enabled Vertex AI API, approved region and models, least-privilege IAM identity, quotas, and secure application credentials.

  4. 04

    Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.

  5. 05

    Run one low-risk test task before connecting sensitive files, repositories, or workspace data.

  • Use official download pages.
  • Review permissions during setup.
  • Keep installers and applications updated.
02

First session

Your first useful Vertex AI session

Learn the interaction loop using a small task with a clear outcome.

  1. 01

    Create an isolated development project with billing alerts, quotas, an approved region, and least-privilege Vertex AI access.

  2. 02

    Define a small benchmark and select a generally available model appropriate for the task and data classification.

  3. 03

    Send a minimal request through the current Gen AI SDK with a pinned model ID, explicit settings, and non-sensitive fixtures.

  4. 04

    Review output support, safety, latency, token use, errors, and retention requirements before widening access or traffic.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.
03

Quality control

Check the quality of Vertex AI output

Establish that an autonomous run did the right thing, not merely that it finished.

  1. 01

    Define what the run should achieve and what it must never touch before granting it a single tool.

  2. 02

    Read the full execution trace: which tools were called, with what arguments, and in what order.

  3. 03

    Verify the side effects directly in the target system rather than trusting the agent's own report of success.

  4. 04

    Confirm failures surfaced as failures — a silent retry loop or a swallowed error is more dangerous than a crash.

  5. 05

    Re-run the same task and compare: an agent that behaves differently across identical runs is not yet production-ready.

  • Verify side effects in the system of record, not in the agent's summary.
  • Require human approval for any irreversible or outward-facing action.
  • Log every tool call so a run can be reconstructed afterwards.
04

Privacy & permissions

Use Vertex AI safely

Bound what an autonomous system can reach before you let it run unattended.

  1. 01

    Enumerate every tool, credential, and system the agent can reach, and remove the ones it does not need.

  2. 02

    Require explicit human approval for irreversible actions: sending, publishing, paying, deleting, or deploying.

  3. 03

    Run against non-production data until behaviour is predictable across repeated runs.

  4. 04

    Set hard limits on spend, iterations, and runtime so a failure loop cannot run unbounded.

  5. 05

    Treat anything the agent reads from the web or a document as data, never as instructions it may follow.

  • Vertex AI capabilities, models, retention conditions, and data locations vary by feature and region. Use isolated projects and service accounts, protect credentials, confirm data governance and zero-retention conditions for the exact feature, enforce quotas and billing alerts, preserve source permissions in retrieval, evaluate safety and grounding, and regression-test every model, prompt, index, and SDK change.
  • Follow your organisation's approved-use policy.
  • Never treat fluent output as authorization to act.
05

Core workflows

Step-by-step ways to use Vertex AI for the work it does best.

Each workflow is a separate guide with its own steps and review checkpoints.

01 WorkflowBenchmark a generative model on Vertex AI

Compare Gemini and approved Model Garden options under consistent data, safety, quality, latency, and cost conditions.

 02 WorkflowBuild a governed RAG workflow on Vertex AI

Ground model responses in approved data while preserving access, source identity, retrieval evaluation, and deletion handling.

06

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.

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