Gemini Enterprise: Build AI Agents & Access GCS Databases with Natural Language

1. Introduction: Office Routines Overwhelmed by Trivialities

In the digital landscape of modern enterprises, we face a paradoxical phenomenon: the more tools we have, the lower our efficiency becomes. Statistics show that the average modern employee frequently switches between more than 254 standalone AI applications, leading to severe "data silos" and fragmented workflows. Every day, employees are bogged down in administrative tasks such as searching for data across systems and consolidating repetitive reports. Here, AI is treated as little more than a "talking search engine."

What if AI evolved beyond a chatbot that merely answers questions? What if it became a "virtual team" capable of autonomously executing complex tasks, operating cross-platform, and even working independently for days? How would the workplace transform? Google’s official launch of Gemini Enterprise is designed precisely to break this deadlock of piecemeal tools, heralded as a major step into the new era of the Agentic Enterprise.

Image 1. Gemini Enterprise Ecosystem

2. More Than an AI Tool: A "Unified Intelligent Operating System" for Enterprises

The core philosophy of Gemini Enterprise is to evolve AI from a simple "toolkit" into an integrated "platform." Google defines it as the "New Gateway to Enterprise AI"—which is not just a portal webpage, but an intelligent hub connecting internal corporate data, third-party applications, and employee collaboration.

Image 2. Unified Management Interface

3. The Era of Citizen Developers: Shaping Custom AI Agents with Natural Language

In the past, workflow automation was the exclusive domain of IT departments. Gemini Enterprise completely breaks down technical barriers through Agent Designer (also known as Workbench), kicking off an era of citizen development characterized by a "low barrier to entry, high output."

1. An Intuitive No-code Experience: Non-technical personnel in marketing, HR, or finance can quickly build an AI agent with specialized skills simply by describing their goals in natural language (e.g., "Help me screen candidates and draft personalized onboarding guides") and checking the required data sources.
2. Logic Optimization: Users can even leverage the "Help me write" feature to further refine and optimize the agent's execution logic.
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Image 3. Agent Designer Interface

4. Breaking Down Walled Gardens: An Open Ecosystem and the BYO-MCP Protocol

Strategically, the most striking aspect of Gemini Enterprise is that it no longer forces enterprises to choose sides between Google and Microsoft. Instead, it demonstrates ultimate compatibility.

Through built-in connectors and the BYO-MCP (Bring Your Own Model Context Protocol) protocol, Gemini agents can securely access data from Microsoft 365 (OneDrive, Outlook, SharePoint) as well as competitors like Salesforce, SAP, and ServiceNow. This makes cross-platform collaboration as smooth as operating a single unified system.

Image 4. Data Interconnection Capabilities (Connectors)

5. Virtual Employees with "Marathon Capabilities": Long-running Agents

Unlike traditional Q&A-style AI, Gemini Enterprise introduces Long-running Agents. These agents can independently handle complex, multi-step workflows—from ad-hoc tasks to recurring duties—such as end-to-end financial reconciliation or deep sales lead scoring.

Image 5. Long-running Agents

Hands-on Application Scenarios

CloudForce, a cloud service provider, is currently facing a major challenge: the company's data is scattered everywhere, creating isolated "data silos." This not only prevents them from accurately forecasting market demand but also turns searching for information into a needle-in-a-haystack ordeal for employees. As a result, teams struggle to make data-driven decisions, and collaboration efficiency plummets.

Gemini Enterprise is born to solve this exact pain point! It connects all of the company's scattered data. Once implemented, production managers and data analysts alike can effortlessly search across systems for sales figures, customer feedback, and inventory status. They can let the AI directly extract key insights, making it easy to grasp the bigger picture of corporate operations.

In this hands-on exercise, we will guide you through the following tasks:

1. Deploy the Gemini Enterprise application.
2. Connect and integrate databases across different platforms, such as Google Cloud Storage.

Step 1: Prepare Gemini Enterprise and GCS Data

In this task, you will retrieve sample documents from Google Cloud Storage (GCS) that have been pre-processed for the LAB environment. Next, you will use this set of files for key content analysis within the Gemini Enterprise application.

1. Go to the Google Cloud Console navigation menu ☰.
2. Click Cloud Storage > Buckets. (The figure below shows the contents of the Cloud Storage bucket.)

Step 2: Create a Gemini Enterprise App

In this section, you will create an application within the enterprise environment and set Google Identity as the Identity Provider (IdP) to perform a one-time initial setup for Gemini Enterprise.

Configure Gemini Enterprise

1. Search for Gemini Enterprise at the top of the Google Cloud Console, then click the search result to go to the platform's landing page.
2. On the Gemini Enterprise card, click Start 30-day Free Trial.

1. After being redirected to the "Create" page, set the Application Name to Gemini Enterprise-XXXXXXX.
2. In the Location section, select Global.
3. Click Create.

The Gemini Enterprise application is now created. The screen should look like this:

Click Set up Identity. Select Use Google Identity, and then click Confirm Employee Identity to configure the Applications Identity Provider.

Quick Tip: When using Google Identity, the GCP project must be added to an Organization.

Step 3: Connect to a Data Store

In this section, you will create a data store to specify the information sources for your Gemini Enterprise application. Once these links are established, Gemini Enterprise can access, index, and search data from systems like Cloud Storage, providing the necessary content for the AI assistant to operate. You will create a data store specifically for Cloud Storage.

Create a Cloud Storage Data Store

1. In the Gemini Enterprise application menu, select Connected Data Stores from the left navigation pane.
2. Select Add Data Store.
3. Locate the Cloud Storage card and click Select.
4. Select the Unstructured documents (PDF, HTML, TXT, etc.) data type.
5. In the Sync Frequency section, select One-time.
6. Finally, select Browse, navigate to your Cloud Storage bucket, and locate and select the 20260323 subfolder.
7. Click Continue.
8. Keep the default Global value and name the data store Cloud Storage.
9. Select Create.

💡 Quick Tips:

1. Each data store can be concurrently connected and used by multiple applications.
2. Data store configurations are visible to all authorized users, meaning they can be used or deleted by others.
3. Whether it is an exclusive data store created at the application level or a shared data store created at the Gemini Enterprise platform level, the total number displayed in the system overview interface represents the sum of all data stores.

Step 4: Interact with the Gemini Enterprise Agent

Now that the Gemini Enterprise application is configured and linked to your data, let's experience its features in action. In this task, you will interact directly with the agent to test its ability to search for data within uploaded documents, extract specific information, generate content summaries, and perform multi-agent ideation. This hands-on practice will show you how Gemini Enterprise reshapes information retrieval and task management within an organization.

Select Go to Gemini Enterprise to open the application. The homepage should look like this:

Users can easily find the most useful content and activities on this homepage, which includes several sections:

1. Agents: Create your own or use Google-provided agents to conduct research, iterate on ideas, and more.
2. Brainstorming: Create multi-agent ideation sessions to brainstorm alongside a team of agents.
3. NotebookLM: An AI-powered research and writing assistant, perfect for use with uploaded sources.
4. Deep Research: Gemini's agentic feature that automatically browses hundreds of websites, evaluates search results, and generates comprehensive, multi-page reports in minutes.

You can use the dropdown menu to filter and search only corporate data (RAG) connectors, restricting the scope to your configured internal data. By default, it will also search via Google Search (this behavior can be changed).

When a Bucket is used as a source, the agent will access the entire data source location.

If no source data is available, the agent will directly reply that the data cannot be found.

Conclusion

An enterprise's competitiveness is no longer about who owns more software tools, but who can be the first to evolve into an Agentic Enterprise. The mission of Gemini Enterprise is simple: to completely liberate your team from the administrative quagmire.

When tedious data collection, report consolidation, and cross-platform tracking are handed over to 24/7 AI agents, you as a leader can dedicate your team's most valuable time and energy to strategic positioning and innovation that truly drives revenue. This is not just an upgrade to your workflow; it is a redefinition of your core competitiveness.

Stop letting your team's talent be consumed by trivialities! Looking for AI and cloud solutions that deliver real results? Contact us today, and let our professional CloudForce team tailor the perfect enterprise upgrade plan for you!