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Atlas/Google Gemini Enterprise / Vertex AI
Vendor dossier / Hyperscaler

Google Gemini Enterprise Agent Platform

Google Cloud’s agent development and operations platform, now incorporating the former Vertex AI surface.

REVIEWED 23 SEPTEMBER 2026Marketing / Sales / CXNO HANDS-ON BENCHMARK

What it is

Official-source notesHyperscalerestablished

The former Vertex AI page now redirects to Gemini Enterprise Agent Platform. Google describes agent building, scaling, evaluation, governance and model choice, with a separate enterprise app surface. [V1] [V2]

Documented means the statement is present in the cited material. Capability effectiveness, customer outcomes and security assurances have not been independently audited here.

Promoted use cases

  • Build grounded enterprise agents
  • Evaluate and deploy models and workflows
  • Integrate AI with Google Cloud data

Cloud agent services are foundations, not automatically finished growth applications. They can provide model access, runtime, identity, memory and evaluation components. An internal team still needs to define the user experience, policies, integrations, workflow ownership and support model. The correct comparison is often build versus buy, or a complementary stack, rather than a product feature contest.

Pricing and buying model

Published commercial evidence

Consumption-based cloud pricing

Gemini Enterprise app entitlements and Agent Platform consumption are distinct.

Check current commercial source ↗

Budget for implementation, model and tool usage, data, human review and ongoing support. Credit units and outcome definitions differ between vendors. A missing numeric rate is marked unverified rather than replaced with an old third-party estimate.

Collaboration and governance

The platform provides infrastructure and evaluation components. Workflow-specific authorization and human ownership still need an application design.

The relevant unit of control is permission to call a production tool under explicit policy. Inspect who can propose, approve, execute, interrupt and audit that action. Shared seats, shared content and a shared live agent session are different capabilities; require a demonstration of the one your process needs.

Evaluate memory correction, permission revocation and release control for updated instructions. An improvement loop should preserve the original evidence, proposed change, test results and named approver. A safety or security badge alone cannot establish those workflow properties.

Review and customer evidence

No independent review sample was verified in this research pass. Vendor-hosted testimonials are selection-biased customer evidence, not an aggregate rating.

Before relying on a testimonial, confirm the exact product, version, package, workload and baseline. Ask a relevant customer about setup effort, failed cases, ongoing manual work and support after launch. This dossier does not convert customer logos or vendor-hosted awards into independent proof.

Focused feature documentation

Focused documentation check · Sep 2026

Agent Platform consumption and release-stage boundaries

Google Cloud now groups agent building, hosting and evaluation under Gemini Enterprise Agent Platform, formerly Vertex AI surfaces. Its pricing documentation distinguishes runtime machines, model input/output and evaluation tokens. The Gemini Enterprise application seats are a different purchase from building a custom agent on the platform.

Some Managed Agents API evaluation documentation is explicitly Pre-GA and limits use to testing, so a pilot must identify the exact runtime and feature release before making a production claim. Ask for a quote with the model, tool, evaluation, storage and operations meters, and inspect user authorization at the data source. A growth workflow still needs prompts, integrations, approval policy and an owner above these platform components.

Primary sources: Official Agent Platform pricing ↗ · Official managed-agent evaluation status ↗ · Official product platform ↗

Published documentation and public illustrations; account behaviour was not independently tested.

Visual source

A screenshot was not captured for this entry. Open the official visual source ↗. The absence of an image does not affect the evidence status of the sourced notes.

Fit, limitations and proof requests

Editorial assessment

Google’s stack is a build-versus-buy and ecosystem decision. It can underpin a custom growth platform and should not be scored as a weak marketer merely because it is infrastructure.

Priority question: Which services are required beyond the model call to implement shared memory, approvals, traces and rollback?

The evaluation owner should be the platform engineering and business workflow owners. Use reliable business execution at a sustainable total operating cost as the business target. Judge the solution in its own stack role: a cloud runtime, a specialist production tool and a managed AI team can be complementary purchases.

Use-case evaluation plan

Build one complete business workflow

Implement one narrowly scoped marketing, sales or CX process with a real test integration. Include a human approval surface, a state store and a useful trace. Record engineering effort for the entire path instead of counting only the code required to call a model.

Measure: implementation effort, deployment repeatability and accepted task completion.

Operate through failure and change

Simulate a tool timeout, a model change and a permission revocation. Verify safe retries, tenant isolation and rollback. Ask the business owner to inspect the result without relying on a platform engineer to interpret every log entry.

Measure: recovery time, duplicate actions, policy enforcement and operator usability.

Estimate the full operating bill

Measure model calls, runtime, storage, retrieval, tools and monitoring for representative workloads. Add engineering, support and evaluation effort. Compare the estimate with a scoped application or managed-service proposal using the same workload and acceptance criteria.

Measure: cost per accepted task, variance at peak load and ongoing engineering hours.

Detailed comparison

Read the detailed Nagent vs Google Gemini Enterprise / Vertex AI guide for operating models, shared context, governance, cost, evidence gaps and a staged pilot.

Nagent vs Google Gemini Enterprise / Vertex AI

Sources and evidence register

Nagent · Multiplayer AI for growth teamsResearch: 21–29 September 2026 · Public-source analysis, no hands-on benchmark · Sources & method